# WARNING: DO NOT EDIT, AUTO-GENERATED CODE!
# See https://github.com/aws-beam/aws-codegen for more details.

defmodule AWS.Rekognition do
  @moduledoc """
  This is the API Reference for [Amazon Rekognition Image](https://docs.aws.amazon.com/rekognition/latest/dg/images.html), [Amazon Rekognition Custom
  Labels](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/what-is.html),
  [Amazon Rekognition Stored Video](https://docs.aws.amazon.com/rekognition/latest/dg/video.html), [Amazon Rekognition Streaming
  Video](https://docs.aws.amazon.com/rekognition/latest/dg/streaming-video.html).

  It provides descriptions of actions, data types, common
  parameters, and common errors.

  ## Amazon Rekognition Image

    *

  [AssociateFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_AssociateFaces.html) 

    *

  [CompareFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CompareFaces.html)

    *

  [CreateCollection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateCollection.html) 

    *

  [CreateUser](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateUser.html)

    *

  [DeleteCollection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteCollection.html) 

    *

  [DeleteFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteFaces.html)

    *

  [DeleteUser](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteUser.html) 

    *

  [DescribeCollection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeCollection.html)

    *

  [DetectFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectFaces.html) 

    *

  [DetectLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectLabels.html)

    *

  [DetectModerationLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectModerationLabels.html) 

    *

  [DetectProtectiveEquipment](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectProtectiveEquipment.html)

    *

  [DetectText](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectText.html) 

    *

  [DisassociateFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DisassociateFaces.html)

    *

  [GetCelebrityInfo](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetCelebrityInfo.html) 

    *

  [GetMediaAnalysisJob](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetMediaAnalysisJob.html)

    *

  [IndexFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_IndexFaces.html) 

    *

  [ListCollections](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListCollections.html)

    *

  [ListMediaAnalysisJob](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListMediaAnalysisJob.html) 

    *

  [ListFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListFaces.html)

    *

  [ListUsers](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListFaces.html) 

    *

  [RecognizeCelebrities](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_RecognizeCelebrities.html)

    *

  [SearchFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchFaces.html) 

    *

  [SearchFacesByImage](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchFacesByImage.html)

    *

  [SearchUsers](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchUsers.html) 

    *

  [SearchUsersByImage](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_SearchUsersByImage.html)

    *

  [StartMediaAnalysisJob](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartMediaAnalysisJob.html) 

  ## Amazon Rekognition Custom Labels

    *

  [CopyProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CopyProjectVersion.html)

    *

  [CreateDataset](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateDataset.html) 

    *

  [CreateProject](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateProject.html)

    *

  [CreateProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateProjectVersion.html) 

    *

  [DeleteDataset](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteDataset.html)

    *

  [DeleteProject](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteProject.html) 

    *

  [DeleteProjectPolicy](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteProjectPolicy.html)

    *

  [DeleteProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteProjectVersion.html) 

    *

  [DescribeDataset](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeDataset.html)

    *

  [DescribeProjects](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeProjects.html) 

    *

  [DescribeProjectVersions](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeProjectVersions.html)

    *

  [DetectCustomLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectCustomLabels.html) 

    *

  [DistributeDatasetEntries](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DistributeDatasetEntries.html)

    *

  [ListDatasetEntries](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListDatasetEntries.html) 

    *

  [ListDatasetLabels](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListDatasetLabels.html)

    *

  [ListProjectPolicies](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListProjectPolicies.html) 

    *

  [PutProjectPolicy](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_PutProjectPolicy.html)

    *

  [StartProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartProjectVersion.html) 

    *

  [StopProjectVersion](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StopProjectVersion.html)

    *

  [UpdateDatasetEntries](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_UpdateDatasetEntries.html) 

  ## Amazon Rekognition Video Stored Video

    *

  [GetCelebrityRecognition](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetCelebrityRecognition.html)

    *

  [GetContentModeration](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetContentModeration.html) 

    *

  [GetFaceDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetFaceDetection.html)

    *

  [GetFaceSearch](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetFaceSearch.html) 

    *

  [GetLabelDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetLabelDetection.html)

    *

  [GetPersonTracking](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetPersonTracking.html) 

    *

  [GetSegmentDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetSegmentDetection.html)

    *

  [GetTextDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetTextDetection.html) 

    *

  [StartCelebrityRecognition](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartCelebrityRecognition.html)

    *

  [StartContentModeration](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartContentModeration.html) 

    *

  [StartFaceDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartFaceDetection.html)

    *

  [StartFaceSearch](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartFaceSearch.html) 

    *

  [StartLabelDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartLabelDetection.html)

    *

  [StartPersonTracking](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartPersonTracking.html) 

    *

  [StartSegmentDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartSegmentDetection.html)

    *

  [StartTextDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartTextDetection.html) 

  ## Amazon Rekognition Video Streaming Video

    *

  [CreateStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_CreateStreamProcessor.html)

    *

  [DeleteStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DeleteStreamProcessor.html) 

    *

  [DescribeStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DescribeStreamProcessor.html)

    *

  [ListStreamProcessors](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_ListStreamProcessors.html) 

    *

  [StartStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartStreamProcessor.html)

    *

  [StopStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StopStreamProcessor.html) 

    *

  [UpdateStreamProcessor](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_UpdateStreamProcessor.html)
  """

  alias AWS.Client
  alias AWS.Request

  @typedoc """

  ## Example:
      
      create_face_liveness_session_request_settings() :: %{
        "AuditImagesLimit" => integer(),
        "ChallengePreferences" => list(challenge_preference()),
        "OutputConfig" => liveness_output_config()
      }
      
  """
  @type create_face_liveness_session_request_settings() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      video() :: %{
        "S3Object" => s3_object()
      }
      
  """
  @type video() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      searched_user() :: %{
        "UserId" => String.t() | atom()
      }
      
  """
  @type searched_user() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      update_dataset_entries_request() :: %{
        required("Changes") => dataset_changes(),
        required("DatasetArn") => String.t() | atom()
      }
      
  """
  @type update_dataset_entries_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_dataset_labels_request() :: %{
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom(),
        required("DatasetArn") => String.t() | atom()
      }
      
  """
  @type list_dataset_labels_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_media_analysis_job_response() :: %{
        "CompletionTimestamp" => non_neg_integer(),
        "CreationTimestamp" => non_neg_integer(),
        "FailureDetails" => media_analysis_job_failure_details(),
        "Input" => media_analysis_input(),
        "JobId" => String.t() | atom(),
        "JobName" => String.t() | atom(),
        "KmsKeyId" => String.t() | atom(),
        "ManifestSummary" => media_analysis_manifest_summary(),
        "OperationsConfig" => media_analysis_operations_config(),
        "OutputConfig" => media_analysis_output_config(),
        "Results" => media_analysis_results(),
        "Status" => list(any())
      }
      
  """
  @type get_media_analysis_job_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      compared_source_image_face() :: %{
        "BoundingBox" => bounding_box(),
        "Confidence" => float()
      }
      
  """
  @type compared_source_image_face() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      segment_type_info() :: %{
        "ModelVersion" => String.t() | atom(),
        "Type" => list(any())
      }
      
  """
  @type segment_type_info() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      recognize_celebrities_request() :: %{
        required("Image") => image()
      }
      
  """
  @type recognize_celebrities_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_label_detection_request_metadata() :: %{
        "AggregateBy" => list(any()),
        "SortBy" => list(any())
      }
      
  """
  @type get_label_detection_request_metadata() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_text_response() :: %{
        "TextDetections" => list(text_detection()),
        "TextModelVersion" => String.t() | atom()
      }
      
  """
  @type detect_text_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_protective_equipment_response() :: %{
        "Persons" => list(protective_equipment_person()),
        "ProtectiveEquipmentModelVersion" => String.t() | atom(),
        "Summary" => protective_equipment_summary()
      }
      
  """
  @type detect_protective_equipment_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      tag_resource_request() :: %{
        required("ResourceArn") => String.t() | atom(),
        required("Tags") => map()
      }
      
  """
  @type tag_resource_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      project_description() :: %{
        "AutoUpdate" => list(any()),
        "CreationTimestamp" => non_neg_integer(),
        "Datasets" => list(dataset_metadata()),
        "Feature" => list(any()),
        "ProjectArn" => String.t() | atom(),
        "Status" => list(any())
      }
      
  """
  @type project_description() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      update_dataset_entries_response() :: %{}
      
  """
  @type update_dataset_entries_response() :: %{}

  @typedoc """

  ## Example:
      
      start_shot_detection_filter() :: %{
        "MinSegmentConfidence" => float()
      }
      
  """
  @type start_shot_detection_filter() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      delete_faces_request() :: %{
        required("CollectionId") => String.t() | atom(),
        required("FaceIds") => list(String.t() | atom())
      }
      
  """
  @type delete_faces_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      create_stream_processor_response() :: %{
        "StreamProcessorArn" => String.t() | atom()
      }
      
  """
  @type create_stream_processor_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_face_search_response() :: %{
        "JobId" => String.t() | atom()
      }
      
  """
  @type start_face_search_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_labels_image_properties_settings() :: %{
        "MaxDominantColors" => integer()
      }
      
  """
  @type detect_labels_image_properties_settings() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      customization_feature_content_moderation_config() :: %{
        "ConfidenceThreshold" => float()
      }
      
  """
  @type customization_feature_content_moderation_config() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      search_users_response() :: %{
        "FaceModelVersion" => String.t() | atom(),
        "SearchedFace" => searched_face(),
        "SearchedUser" => searched_user(),
        "UserMatches" => list(user_match())
      }
      
  """
  @type search_users_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      matched_user() :: %{
        "UserId" => String.t() | atom(),
        "UserStatus" => list(any())
      }
      
  """
  @type matched_user() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      search_faces_response() :: %{
        "FaceMatches" => list(face_match()),
        "FaceModelVersion" => String.t() | atom(),
        "SearchedFaceId" => String.t() | atom()
      }
      
  """
  @type search_faces_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_label_detection_request() :: %{
        optional("ClientRequestToken") => String.t() | atom(),
        optional("Features") => list(list(any())()),
        optional("JobTag") => String.t() | atom(),
        optional("MinConfidence") => float(),
        optional("NotificationChannel") => notification_channel(),
        optional("Settings") => label_detection_settings(),
        required("Video") => video()
      }
      
  """
  @type start_label_detection_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      delete_dataset_response() :: %{}
      
  """
  @type delete_dataset_response() :: %{}

  @typedoc """

  ## Example:
      
      list_dataset_labels_response() :: %{
        "DatasetLabelDescriptions" => list(dataset_label_description()),
        "NextToken" => String.t() | atom()
      }
      
  """
  @type list_dataset_labels_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      dataset_source() :: %{
        "DatasetArn" => String.t() | atom(),
        "GroundTruthManifest" => ground_truth_manifest()
      }
      
  """
  @type dataset_source() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      training_data_result() :: %{
        "Input" => training_data(),
        "Output" => training_data(),
        "Validation" => validation_data()
      }
      
  """
  @type training_data_result() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      unindexed_face() :: %{
        "FaceDetail" => face_detail(),
        "Reasons" => list(list(any())())
      }
      
  """
  @type unindexed_face() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      geometry() :: %{
        "BoundingBox" => bounding_box(),
        "Polygon" => list(point())
      }
      
  """
  @type geometry() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      dataset_description() :: %{
        "CreationTimestamp" => non_neg_integer(),
        "DatasetStats" => dataset_stats(),
        "LastUpdatedTimestamp" => non_neg_integer(),
        "Status" => list(any()),
        "StatusMessage" => String.t() | atom(),
        "StatusMessageCode" => list(any())
      }
      
  """
  @type dataset_description() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      media_analysis_job_failure_details() :: %{
        "Code" => list(any()),
        "Message" => String.t() | atom()
      }
      
  """
  @type media_analysis_job_failure_details() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      untag_resource_response() :: %{}
      
  """
  @type untag_resource_response() :: %{}

  @typedoc """

  ## Example:
      
      stream_processor() :: %{
        "Name" => String.t() | atom(),
        "Status" => list(any())
      }
      
  """
  @type stream_processor() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      media_analysis_operations_config() :: %{
        "DetectModerationLabels" => media_analysis_detect_moderation_labels_config()
      }
      
  """
  @type media_analysis_operations_config() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_media_analysis_job_request() :: %{
        optional("ClientRequestToken") => String.t() | atom(),
        optional("JobName") => String.t() | atom(),
        optional("KmsKeyId") => String.t() | atom(),
        required("Input") => media_analysis_input(),
        required("OperationsConfig") => media_analysis_operations_config(),
        required("OutputConfig") => media_analysis_output_config()
      }
      
  """
  @type start_media_analysis_job_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      resource_in_use_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type resource_in_use_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      index_faces_request() :: %{
        optional("DetectionAttributes") => list(list(any())()),
        optional("ExternalImageId") => String.t() | atom(),
        optional("MaxFaces") => integer(),
        optional("QualityFilter") => list(any()),
        required("CollectionId") => String.t() | atom(),
        required("Image") => image()
      }
      
  """
  @type index_faces_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      person_match() :: %{
        "FaceMatches" => list(face_match()),
        "Person" => person_detail(),
        "Timestamp" => float()
      }
      
  """
  @type person_match() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      describe_projects_response() :: %{
        "NextToken" => String.t() | atom(),
        "ProjectDescriptions" => list(project_description())
      }
      
  """
  @type describe_projects_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      delete_faces_response() :: %{
        "DeletedFaces" => list(String.t() | atom()),
        "UnsuccessfulFaceDeletions" => list(unsuccessful_face_deletion())
      }
      
  """
  @type delete_faces_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_faces_response() :: %{
        "FaceDetails" => list(face_detail()),
        "OrientationCorrection" => list(any())
      }
      
  """
  @type detect_faces_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      describe_stream_processor_response() :: %{
        "CreationTimestamp" => non_neg_integer(),
        "DataSharingPreference" => stream_processor_data_sharing_preference(),
        "Input" => stream_processor_input(),
        "KmsKeyId" => String.t() | atom(),
        "LastUpdateTimestamp" => non_neg_integer(),
        "Name" => String.t() | atom(),
        "NotificationChannel" => stream_processor_notification_channel(),
        "Output" => stream_processor_output(),
        "RegionsOfInterest" => list(region_of_interest()),
        "RoleArn" => String.t() | atom(),
        "Settings" => stream_processor_settings(),
        "Status" => list(any()),
        "StatusMessage" => String.t() | atom(),
        "StreamProcessorArn" => String.t() | atom()
      }
      
  """
  @type describe_stream_processor_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      stream_processing_start_selector() :: %{
        "KVSStreamStartSelector" => kinesis_video_stream_start_selector()
      }
      
  """
  @type stream_processing_start_selector() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      versions() :: %{
        "Maximum" => String.t() | atom(),
        "Minimum" => String.t() | atom()
      }
      
  """
  @type versions() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      smile() :: %{
        "Confidence" => float(),
        "Value" => boolean()
      }
      
  """
  @type smile() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      connected_home_settings() :: %{
        "Labels" => list(String.t() | atom()),
        "MinConfidence" => float()
      }
      
  """
  @type connected_home_settings() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_celebrity_info_response() :: %{
        "KnownGender" => known_gender(),
        "Name" => String.t() | atom(),
        "Urls" => list(String.t() | atom())
      }
      
  """
  @type get_celebrity_info_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      stop_project_version_request() :: %{
        required("ProjectVersionArn") => String.t() | atom()
      }
      
  """
  @type stop_project_version_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      media_analysis_output_config() :: %{
        "S3Bucket" => String.t() | atom(),
        "S3KeyPrefix" => String.t() | atom()
      }
      
  """
  @type media_analysis_output_config() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      person_detail() :: %{
        "BoundingBox" => bounding_box(),
        "Face" => face_detail(),
        "Index" => float()
      }
      
  """
  @type person_detail() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_content_moderation_request_metadata() :: %{
        "AggregateBy" => list(any()),
        "SortBy" => list(any())
      }
      
  """
  @type get_content_moderation_request_metadata() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_face_search_request() :: %{
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom(),
        optional("SortBy") => list(any()),
        required("JobId") => String.t() | atom()
      }
      
  """
  @type get_face_search_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_stream_processor_response() :: %{
        "SessionId" => String.t() | atom()
      }
      
  """
  @type start_stream_processor_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      associate_faces_response() :: %{
        "AssociatedFaces" => list(associated_face()),
        "UnsuccessfulFaceAssociations" => list(unsuccessful_face_association()),
        "UserStatus" => list(any())
      }
      
  """
  @type associate_faces_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      unsuccessful_face_deletion() :: %{
        "FaceId" => String.t() | atom(),
        "Reasons" => list(list(any())()),
        "UserId" => String.t() | atom()
      }
      
  """
  @type unsuccessful_face_deletion() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_face_detection_response() :: %{
        "JobId" => String.t() | atom()
      }
      
  """
  @type start_face_detection_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      protective_equipment_person() :: %{
        "BodyParts" => list(protective_equipment_body_part()),
        "BoundingBox" => bounding_box(),
        "Confidence" => float(),
        "Id" => integer()
      }
      
  """
  @type protective_equipment_person() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      eye_direction() :: %{
        "Confidence" => float(),
        "Pitch" => float(),
        "Yaw" => float()
      }
      
  """
  @type eye_direction() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_text_detection_request() :: %{
        optional("ClientRequestToken") => String.t() | atom(),
        optional("Filters") => start_text_detection_filters(),
        optional("JobTag") => String.t() | atom(),
        optional("NotificationChannel") => notification_channel(),
        required("Video") => video()
      }
      
  """
  @type start_text_detection_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_content_moderation_request() :: %{
        optional("AggregateBy") => list(any()),
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom(),
        optional("SortBy") => list(any()),
        required("JobId") => String.t() | atom()
      }
      
  """
  @type get_content_moderation_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      s3_object() :: %{
        "Bucket" => String.t() | atom(),
        "Name" => String.t() | atom(),
        "Version" => String.t() | atom()
      }
      
  """
  @type s3_object() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      connected_home_settings_for_update() :: %{
        "Labels" => list(String.t() | atom()),
        "MinConfidence" => float()
      }
      
  """
  @type connected_home_settings_for_update() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      media_analysis_manifest_summary() :: %{
        "S3Object" => s3_object()
      }
      
  """
  @type media_analysis_manifest_summary() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_moderation_labels_response() :: %{
        "ContentTypes" => list(content_type()),
        "HumanLoopActivationOutput" => human_loop_activation_output(),
        "ModerationLabels" => list(moderation_label()),
        "ModerationModelVersion" => String.t() | atom(),
        "ProjectVersion" => String.t() | atom()
      }
      
  """
  @type detect_moderation_labels_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      image_too_large_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type image_too_large_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      search_faces_request() :: %{
        optional("FaceMatchThreshold") => float(),
        optional("MaxFaces") => integer(),
        required("CollectionId") => String.t() | atom(),
        required("FaceId") => String.t() | atom()
      }
      
  """
  @type search_faces_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_project_version_response() :: %{
        "Status" => list(any())
      }
      
  """
  @type start_project_version_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      disassociate_faces_response() :: %{
        "DisassociatedFaces" => list(disassociated_face()),
        "UnsuccessfulFaceDisassociations" => list(unsuccessful_face_disassociation()),
        "UserStatus" => list(any())
      }
      
  """
  @type disassociate_faces_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      stream_processor_output() :: %{
        "KinesisDataStream" => kinesis_data_stream(),
        "S3Destination" => s3_destination()
      }
      
  """
  @type stream_processor_output() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      training_data() :: %{
        "Assets" => list(asset())
      }
      
  """
  @type training_data() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      create_project_version_response() :: %{
        "ProjectVersionArn" => String.t() | atom()
      }
      
  """
  @type create_project_version_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detection_filter() :: %{
        "MinBoundingBoxHeight" => float(),
        "MinBoundingBoxWidth" => float(),
        "MinConfidence" => float()
      }
      
  """
  @type detection_filter() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      untag_resource_request() :: %{
        required("ResourceArn") => String.t() | atom(),
        required("TagKeys") => list(String.t() | atom())
      }
      
  """
  @type untag_resource_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      dataset_label_description() :: %{
        "LabelName" => String.t() | atom(),
        "LabelStats" => dataset_label_stats()
      }
      
  """
  @type dataset_label_description() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      known_gender() :: %{
        "Type" => list(any())
      }
      
  """
  @type known_gender() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      segment_detection() :: %{
        "DurationFrames" => float(),
        "DurationMillis" => float(),
        "DurationSMPTE" => String.t() | atom(),
        "EndFrameNumber" => float(),
        "EndTimecodeSMPTE" => String.t() | atom(),
        "EndTimestampMillis" => float(),
        "ShotSegment" => shot_segment(),
        "StartFrameNumber" => float(),
        "StartTimecodeSMPTE" => String.t() | atom(),
        "StartTimestampMillis" => float(),
        "TechnicalCueSegment" => technical_cue_segment(),
        "Type" => list(any())
      }
      
  """
  @type segment_detection() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      searched_face() :: %{
        "FaceId" => String.t() | atom()
      }
      
  """
  @type searched_face() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_faces_request() :: %{
        optional("FaceIds") => list(String.t() | atom()),
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom(),
        optional("UserId") => String.t() | atom(),
        required("CollectionId") => String.t() | atom()
      }
      
  """
  @type list_faces_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_text_request() :: %{
        optional("Filters") => detect_text_filters(),
        required("Image") => image()
      }
      
  """
  @type detect_text_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_label_detection_response() :: %{
        "GetRequestMetadata" => get_label_detection_request_metadata(),
        "JobId" => String.t() | atom(),
        "JobStatus" => list(any()),
        "JobTag" => String.t() | atom(),
        "LabelModelVersion" => String.t() | atom(),
        "Labels" => list(label_detection()),
        "NextToken" => String.t() | atom(),
        "StatusMessage" => String.t() | atom(),
        "Video" => video(),
        "VideoMetadata" => video_metadata()
      }
      
  """
  @type get_label_detection_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      moderation_label() :: %{
        "Confidence" => float(),
        "Name" => String.t() | atom(),
        "ParentName" => String.t() | atom(),
        "TaxonomyLevel" => integer()
      }
      
  """
  @type moderation_label() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_collections_response() :: %{
        "CollectionIds" => list(String.t() | atom()),
        "FaceModelVersions" => list(String.t() | atom()),
        "NextToken" => String.t() | atom()
      }
      
  """
  @type list_collections_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      associated_face() :: %{
        "FaceId" => String.t() | atom()
      }
      
  """
  @type associated_face() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      put_project_policy_response() :: %{
        "PolicyRevisionId" => String.t() | atom()
      }
      
  """
  @type put_project_policy_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      compare_faces_request() :: %{
        optional("QualityFilter") => list(any()),
        optional("SimilarityThreshold") => float(),
        required("SourceImage") => image(),
        required("TargetImage") => image()
      }
      
  """
  @type compare_faces_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      region_of_interest() :: %{
        "BoundingBox" => bounding_box(),
        "Polygon" => list(point())
      }
      
  """
  @type region_of_interest() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      distribute_dataset_entries_request() :: %{
        required("Datasets") => list(distribute_dataset())
      }
      
  """
  @type distribute_dataset_entries_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      describe_projects_request() :: %{
        optional("Features") => list(list(any())()),
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom(),
        optional("ProjectNames") => list(String.t() | atom())
      }
      
  """
  @type describe_projects_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      content_type() :: %{
        "Confidence" => float(),
        "Name" => String.t() | atom()
      }
      
  """
  @type content_type() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      emotion() :: %{
        "Confidence" => float(),
        "Type" => list(any())
      }
      
  """
  @type emotion() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      testing_data() :: %{
        "Assets" => list(asset()),
        "AutoCreate" => boolean()
      }
      
  """
  @type testing_data() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_face_liveness_session_results_request() :: %{
        required("SessionId") => String.t() | atom()
      }
      
  """
  @type get_face_liveness_session_results_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      notification_channel() :: %{
        "RoleArn" => String.t() | atom(),
        "SNSTopicArn" => String.t() | atom()
      }
      
  """
  @type notification_channel() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_celebrity_recognition_response() :: %{
        "Celebrities" => list(celebrity_recognition()),
        "JobId" => String.t() | atom(),
        "JobStatus" => list(any()),
        "JobTag" => String.t() | atom(),
        "NextToken" => String.t() | atom(),
        "StatusMessage" => String.t() | atom(),
        "Video" => video(),
        "VideoMetadata" => video_metadata()
      }
      
  """
  @type get_celebrity_recognition_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      point() :: %{
        "X" => float(),
        "Y" => float()
      }
      
  """
  @type point() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      create_dataset_response() :: %{
        "DatasetArn" => String.t() | atom()
      }
      
  """
  @type create_dataset_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      update_stream_processor_response() :: %{}
      
  """
  @type update_stream_processor_response() :: %{}

  @typedoc """

  ## Example:
      
      human_loop_activation_output() :: %{
        "HumanLoopActivationConditionsEvaluationResults" => String.t() | atom(),
        "HumanLoopActivationReasons" => list(String.t() | atom()),
        "HumanLoopArn" => String.t() | atom()
      }
      
  """
  @type human_loop_activation_output() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      eye_open() :: %{
        "Confidence" => float(),
        "Value" => boolean()
      }
      
  """
  @type eye_open() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      provisioned_throughput_exceeded_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type provisioned_throughput_exceeded_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      invalid_policy_revision_id_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type invalid_policy_revision_id_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_face_detection_request() :: %{
        optional("ClientRequestToken") => String.t() | atom(),
        optional("FaceAttributes") => list(any()),
        optional("JobTag") => String.t() | atom(),
        optional("NotificationChannel") => notification_channel(),
        required("Video") => video()
      }
      
  """
  @type start_face_detection_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      create_project_response() :: %{
        "ProjectArn" => String.t() | atom()
      }
      
  """
  @type create_project_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      landmark() :: %{
        "Type" => list(any()),
        "X" => float(),
        "Y" => float()
      }
      
  """
  @type landmark() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      stop_stream_processor_response() :: %{}
      
  """
  @type stop_stream_processor_response() :: %{}

  @typedoc """

  ## Example:
      
      conflict_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type conflict_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      dominant_color() :: %{
        "Blue" => integer(),
        "CSSColor" => String.t() | atom(),
        "Green" => integer(),
        "HexCode" => String.t() | atom(),
        "PixelPercent" => float(),
        "Red" => integer(),
        "SimplifiedColor" => String.t() | atom()
      }
      
  """
  @type dominant_color() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      resource_not_found_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type resource_not_found_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      asset() :: %{
        "GroundTruthManifest" => ground_truth_manifest()
      }
      
  """
  @type asset() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      dataset_changes() :: %{
        "GroundTruth" => binary()
      }
      
  """
  @type dataset_changes() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      associate_faces_request() :: %{
        optional("ClientRequestToken") => String.t() | atom(),
        optional("UserMatchThreshold") => float(),
        required("CollectionId") => String.t() | atom(),
        required("FaceIds") => list(String.t() | atom()),
        required("UserId") => String.t() | atom()
      }
      
  """
  @type associate_faces_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_collections_request() :: %{
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom()
      }
      
  """
  @type list_collections_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      delete_collection_request() :: %{
        required("CollectionId") => String.t() | atom()
      }
      
  """
  @type delete_collection_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_media_analysis_jobs_response() :: %{
        "MediaAnalysisJobs" => list(media_analysis_job_description()),
        "NextToken" => String.t() | atom()
      }
      
  """
  @type list_media_analysis_jobs_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      face_occluded() :: %{
        "Confidence" => float(),
        "Value" => boolean()
      }
      
  """
  @type face_occluded() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      stream_processor_settings_for_update() :: %{
        "ConnectedHomeForUpdate" => connected_home_settings_for_update()
      }
      
  """
  @type stream_processor_settings_for_update() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_labels_image_properties() :: %{
        "Background" => detect_labels_image_background(),
        "DominantColors" => list(dominant_color()),
        "Foreground" => detect_labels_image_foreground(),
        "Quality" => detect_labels_image_quality()
      }
      
  """
  @type detect_labels_image_properties() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      searched_face_details() :: %{
        "FaceDetail" => face_detail()
      }
      
  """
  @type searched_face_details() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      ground_truth_manifest() :: %{
        "S3Object" => s3_object()
      }
      
  """
  @type ground_truth_manifest() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      label_category() :: %{
        "Name" => String.t() | atom()
      }
      
  """
  @type label_category() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_text_detection_request() :: %{
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom(),
        required("JobId") => String.t() | atom()
      }
      
  """
  @type get_text_detection_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_labels_request() :: %{
        optional("Features") => list(list(any())()),
        optional("MaxLabels") => integer(),
        optional("MinConfidence") => float(),
        optional("Settings") => detect_labels_settings(),
        required("Image") => image()
      }
      
  """
  @type detect_labels_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_project_policies_request() :: %{
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom(),
        required("ProjectArn") => String.t() | atom()
      }
      
  """
  @type list_project_policies_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_stream_processors_response() :: %{
        "NextToken" => String.t() | atom(),
        "StreamProcessors" => list(stream_processor())
      }
      
  """
  @type list_stream_processors_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      service_quota_exceeded_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type service_quota_exceeded_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_stream_processors_request() :: %{
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom()
      }
      
  """
  @type list_stream_processors_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      mouth_open() :: %{
        "Confidence" => float(),
        "Value" => boolean()
      }
      
  """
  @type mouth_open() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_technical_cue_detection_filter() :: %{
        "BlackFrame" => black_frame(),
        "MinSegmentConfidence" => float()
      }
      
  """
  @type start_technical_cue_detection_filter() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      idempotent_parameter_mismatch_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type idempotent_parameter_mismatch_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      delete_project_policy_response() :: %{}
      
  """
  @type delete_project_policy_response() :: %{}

  @typedoc """

  ## Example:
      
      start_segment_detection_filters() :: %{
        "ShotFilter" => start_shot_detection_filter(),
        "TechnicalCueFilter" => start_technical_cue_detection_filter()
      }
      
  """
  @type start_segment_detection_filters() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_custom_labels_request() :: %{
        optional("MaxResults") => integer(),
        optional("MinConfidence") => float(),
        required("Image") => image(),
        required("ProjectVersionArn") => String.t() | atom()
      }
      
  """
  @type detect_custom_labels_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_users_response() :: %{
        "NextToken" => String.t() | atom(),
        "Users" => list(user())
      }
      
  """
  @type list_users_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      describe_collection_request() :: %{
        required("CollectionId") => String.t() | atom()
      }
      
  """
  @type describe_collection_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_face_detection_response() :: %{
        "Faces" => list(face_detection()),
        "JobId" => String.t() | atom(),
        "JobStatus" => list(any()),
        "JobTag" => String.t() | atom(),
        "NextToken" => String.t() | atom(),
        "StatusMessage" => String.t() | atom(),
        "Video" => video(),
        "VideoMetadata" => video_metadata()
      }
      
  """
  @type get_face_detection_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      dataset_stats() :: %{
        "ErrorEntries" => integer(),
        "LabeledEntries" => integer(),
        "TotalEntries" => integer(),
        "TotalLabels" => integer()
      }
      
  """
  @type dataset_stats() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      label_detection() :: %{
        "DurationMillis" => float(),
        "EndTimestampMillis" => float(),
        "Label" => label(),
        "StartTimestampMillis" => float(),
        "Timestamp" => float()
      }
      
  """
  @type label_detection() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_text_filters() :: %{
        "RegionsOfInterest" => list(region_of_interest()),
        "WordFilter" => detection_filter()
      }
      
  """
  @type detect_text_filters() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_dataset_entries_request() :: %{
        optional("ContainsLabels") => list(String.t() | atom()),
        optional("HasErrors") => boolean(),
        optional("Labeled") => boolean(),
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom(),
        optional("SourceRefContains") => String.t() | atom(),
        required("DatasetArn") => String.t() | atom()
      }
      
  """
  @type list_dataset_entries_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_custom_labels_response() :: %{
        "CustomLabels" => list(custom_label())
      }
      
  """
  @type detect_custom_labels_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      search_faces_by_image_request() :: %{
        optional("FaceMatchThreshold") => float(),
        optional("MaxFaces") => integer(),
        optional("QualityFilter") => list(any()),
        required("CollectionId") => String.t() | atom(),
        required("Image") => image()
      }
      
  """
  @type search_faces_by_image_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      search_users_by_image_request() :: %{
        optional("MaxUsers") => integer(),
        optional("QualityFilter") => list(any()),
        optional("UserMatchThreshold") => float(),
        required("CollectionId") => String.t() | atom(),
        required("Image") => image()
      }
      
  """
  @type search_users_by_image_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_celebrity_recognition_request() :: %{
        optional("ClientRequestToken") => String.t() | atom(),
        optional("JobTag") => String.t() | atom(),
        optional("NotificationChannel") => notification_channel(),
        required("Video") => video()
      }
      
  """
  @type start_celebrity_recognition_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      create_collection_request() :: %{
        optional("Tags") => map(),
        required("CollectionId") => String.t() | atom()
      }
      
  """
  @type create_collection_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      compare_faces_match() :: %{
        "Face" => compared_face(),
        "Similarity" => float()
      }
      
  """
  @type compare_faces_match() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      kinesis_video_stream_start_selector() :: %{
        "FragmentNumber" => String.t() | atom(),
        "ProducerTimestamp" => float()
      }
      
  """
  @type kinesis_video_stream_start_selector() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      project_version_description() :: %{
        "BaseModelVersion" => String.t() | atom(),
        "BillableTrainingTimeInSeconds" => float(),
        "CreationTimestamp" => non_neg_integer(),
        "EvaluationResult" => evaluation_result(),
        "Feature" => list(any()),
        "FeatureConfig" => customization_feature_config(),
        "KmsKeyId" => String.t() | atom(),
        "ManifestSummary" => ground_truth_manifest(),
        "MaxInferenceUnits" => integer(),
        "MinInferenceUnits" => integer(),
        "OutputConfig" => output_config(),
        "ProjectVersionArn" => String.t() | atom(),
        "SourceProjectVersionArn" => String.t() | atom(),
        "Status" => list(any()),
        "StatusMessage" => String.t() | atom(),
        "TestingDataResult" => testing_data_result(),
        "TrainingDataResult" => training_data_result(),
        "TrainingEndTimestamp" => non_neg_integer(),
        "VersionDescription" => String.t() | atom()
      }
      
  """
  @type project_version_description() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_person_tracking_response() :: %{
        "JobId" => String.t() | atom()
      }
      
  """
  @type start_person_tracking_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_tags_for_resource_response() :: %{
        "Tags" => map()
      }
      
  """
  @type list_tags_for_resource_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      dataset_label_stats() :: %{
        "BoundingBoxCount" => integer(),
        "EntryCount" => integer()
      }
      
  """
  @type dataset_label_stats() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      content_moderation_detection() :: %{
        "ContentTypes" => list(content_type()),
        "DurationMillis" => float(),
        "EndTimestampMillis" => float(),
        "ModerationLabel" => moderation_label(),
        "StartTimestampMillis" => float(),
        "Timestamp" => float()
      }
      
  """
  @type content_moderation_detection() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      media_analysis_results() :: %{
        "ModelVersions" => media_analysis_model_versions(),
        "S3Object" => s3_object()
      }
      
  """
  @type media_analysis_results() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_label_detection_request() :: %{
        optional("AggregateBy") => list(any()),
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom(),
        optional("SortBy") => list(any()),
        required("JobId") => String.t() | atom()
      }
      
  """
  @type get_label_detection_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_protective_equipment_request() :: %{
        optional("SummarizationAttributes") => protective_equipment_summarization_attributes(),
        required("Image") => image()
      }
      
  """
  @type detect_protective_equipment_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      search_users_request() :: %{
        optional("FaceId") => String.t() | atom(),
        optional("MaxUsers") => integer(),
        optional("UserId") => String.t() | atom(),
        optional("UserMatchThreshold") => float(),
        required("CollectionId") => String.t() | atom()
      }
      
  """
  @type search_users_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      search_users_by_image_response() :: %{
        "FaceModelVersion" => String.t() | atom(),
        "SearchedFace" => searched_face_details(),
        "UnsearchedFaces" => list(unsearched_face()),
        "UserMatches" => list(user_match())
      }
      
  """
  @type search_users_by_image_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      face_detail() :: %{
        "AgeRange" => age_range(),
        "Beard" => beard(),
        "BoundingBox" => bounding_box(),
        "Confidence" => float(),
        "Emotions" => list(emotion()),
        "EyeDirection" => eye_direction(),
        "Eyeglasses" => eyeglasses(),
        "EyesOpen" => eye_open(),
        "FaceOccluded" => face_occluded(),
        "Gender" => gender(),
        "Landmarks" => list(landmark()),
        "MouthOpen" => mouth_open(),
        "Mustache" => mustache(),
        "Pose" => pose(),
        "Quality" => image_quality(),
        "Smile" => smile(),
        "Sunglasses" => sunglasses()
      }
      
  """
  @type face_detail() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      black_frame() :: %{
        "MaxPixelThreshold" => float(),
        "MinCoveragePercentage" => float()
      }
      
  """
  @type black_frame() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_labels_response() :: %{
        "ImageProperties" => detect_labels_image_properties(),
        "LabelModelVersion" => String.t() | atom(),
        "Labels" => list(label()),
        "OrientationCorrection" => list(any())
      }
      
  """
  @type detect_labels_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      testing_data_result() :: %{
        "Input" => testing_data(),
        "Output" => testing_data(),
        "Validation" => validation_data()
      }
      
  """
  @type testing_data_result() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      unsearched_face() :: %{
        "FaceDetails" => face_detail(),
        "Reasons" => list(list(any())())
      }
      
  """
  @type unsearched_face() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      face() :: %{
        "BoundingBox" => bounding_box(),
        "Confidence" => float(),
        "ExternalImageId" => String.t() | atom(),
        "FaceId" => String.t() | atom(),
        "ImageId" => String.t() | atom(),
        "IndexFacesModelVersion" => String.t() | atom(),
        "UserId" => String.t() | atom()
      }
      
  """
  @type face() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_project_policies_response() :: %{
        "NextToken" => String.t() | atom(),
        "ProjectPolicies" => list(project_policy())
      }
      
  """
  @type list_project_policies_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      create_project_version_request() :: %{
        optional("FeatureConfig") => customization_feature_config(),
        optional("KmsKeyId") => String.t() | atom(),
        optional("Tags") => map(),
        optional("TestingData") => testing_data(),
        optional("TrainingData") => training_data(),
        optional("VersionDescription") => String.t() | atom(),
        required("OutputConfig") => output_config(),
        required("ProjectArn") => String.t() | atom(),
        required("VersionName") => String.t() | atom()
      }
      
  """
  @type create_project_version_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      beard() :: %{
        "Confidence" => float(),
        "Value" => boolean()
      }
      
  """
  @type beard() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      distribute_dataset_entries_response() :: %{}
      
  """
  @type distribute_dataset_entries_response() :: %{}

  @typedoc """

  ## Example:
      
      protective_equipment_summarization_attributes() :: %{
        "MinConfidence" => float(),
        "RequiredEquipmentTypes" => list(list(any())())
      }
      
  """
  @type protective_equipment_summarization_attributes() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      delete_user_request() :: %{
        optional("ClientRequestToken") => String.t() | atom(),
        required("CollectionId") => String.t() | atom(),
        required("UserId") => String.t() | atom()
      }
      
  """
  @type delete_user_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      s3_destination() :: %{
        "Bucket" => String.t() | atom(),
        "KeyPrefix" => String.t() | atom()
      }
      
  """
  @type s3_destination() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_segment_detection_response() :: %{
        "AudioMetadata" => list(audio_metadata()),
        "JobId" => String.t() | atom(),
        "JobStatus" => list(any()),
        "JobTag" => String.t() | atom(),
        "NextToken" => String.t() | atom(),
        "Segments" => list(segment_detection()),
        "SelectedSegmentTypes" => list(segment_type_info()),
        "StatusMessage" => String.t() | atom(),
        "Video" => video(),
        "VideoMetadata" => list(video_metadata())
      }
      
  """
  @type get_segment_detection_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      custom_label() :: %{
        "Confidence" => float(),
        "Geometry" => geometry(),
        "Name" => String.t() | atom()
      }
      
  """
  @type custom_label() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      celebrity() :: %{
        "Face" => compared_face(),
        "Id" => String.t() | atom(),
        "KnownGender" => known_gender(),
        "MatchConfidence" => float(),
        "Name" => String.t() | atom(),
        "Urls" => list(String.t() | atom())
      }
      
  """
  @type celebrity() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      delete_user_response() :: %{}
      
  """
  @type delete_user_response() :: %{}

  @typedoc """

  ## Example:
      
      get_celebrity_recognition_request() :: %{
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom(),
        optional("SortBy") => list(any()),
        required("JobId") => String.t() | atom()
      }
      
  """
  @type get_celebrity_recognition_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      challenge_preference() :: %{
        "Type" => list(any()),
        "Versions" => versions()
      }
      
  """
  @type challenge_preference() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      celebrity_recognition() :: %{
        "Celebrity" => celebrity_detail(),
        "Timestamp" => float()
      }
      
  """
  @type celebrity_recognition() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      recognize_celebrities_response() :: %{
        "CelebrityFaces" => list(celebrity()),
        "OrientationCorrection" => list(any()),
        "UnrecognizedFaces" => list(compared_face())
      }
      
  """
  @type recognize_celebrities_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      challenge() :: %{
        "Type" => list(any()),
        "Version" => String.t() | atom()
      }
      
  """
  @type challenge() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      face_search_settings() :: %{
        "CollectionId" => String.t() | atom(),
        "FaceMatchThreshold" => float()
      }
      
  """
  @type face_search_settings() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      create_face_liveness_session_response() :: %{
        "SessionId" => String.t() | atom()
      }
      
  """
  @type create_face_liveness_session_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      stream_processor_data_sharing_preference() :: %{
        "OptIn" => boolean()
      }
      
  """
  @type stream_processor_data_sharing_preference() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      describe_collection_response() :: %{
        "CollectionARN" => String.t() | atom(),
        "CreationTimestamp" => non_neg_integer(),
        "FaceCount" => float(),
        "FaceModelVersion" => String.t() | atom(),
        "UserCount" => float()
      }
      
  """
  @type describe_collection_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      stream_processor_settings() :: %{
        "ConnectedHome" => connected_home_settings(),
        "FaceSearch" => face_search_settings()
      }
      
  """
  @type stream_processor_settings() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      create_collection_response() :: %{
        "CollectionArn" => String.t() | atom(),
        "FaceModelVersion" => String.t() | atom(),
        "StatusCode" => integer()
      }
      
  """
  @type create_collection_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      age_range() :: %{
        "High" => integer(),
        "Low" => integer()
      }
      
  """
  @type age_range() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      human_loop_data_attributes() :: %{
        "ContentClassifiers" => list(list(any())())
      }
      
  """
  @type human_loop_data_attributes() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      protective_equipment_body_part() :: %{
        "Confidence" => float(),
        "EquipmentDetections" => list(equipment_detection()),
        "Name" => list(any())
      }
      
  """
  @type protective_equipment_body_part() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      delete_project_response() :: %{
        "Status" => list(any())
      }
      
  """
  @type delete_project_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      put_project_policy_request() :: %{
        optional("PolicyRevisionId") => String.t() | atom(),
        required("PolicyDocument") => String.t() | atom(),
        required("PolicyName") => String.t() | atom(),
        required("ProjectArn") => String.t() | atom()
      }
      
  """
  @type put_project_policy_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      bounding_box() :: %{
        "Height" => float(),
        "Left" => float(),
        "Top" => float(),
        "Width" => float()
      }
      
  """
  @type bounding_box() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      delete_stream_processor_request() :: %{
        required("Name") => String.t() | atom()
      }
      
  """
  @type delete_stream_processor_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      unsuccessful_face_disassociation() :: %{
        "FaceId" => String.t() | atom(),
        "Reasons" => list(list(any())()),
        "UserId" => String.t() | atom()
      }
      
  """
  @type unsuccessful_face_disassociation() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      stop_stream_processor_request() :: %{
        required("Name") => String.t() | atom()
      }
      
  """
  @type stop_stream_processor_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      face_match() :: %{
        "Face" => face(),
        "Similarity" => float()
      }
      
  """
  @type face_match() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      media_analysis_job_description() :: %{
        "CompletionTimestamp" => non_neg_integer(),
        "CreationTimestamp" => non_neg_integer(),
        "FailureDetails" => media_analysis_job_failure_details(),
        "Input" => media_analysis_input(),
        "JobId" => String.t() | atom(),
        "JobName" => String.t() | atom(),
        "KmsKeyId" => String.t() | atom(),
        "ManifestSummary" => media_analysis_manifest_summary(),
        "OperationsConfig" => media_analysis_operations_config(),
        "OutputConfig" => media_analysis_output_config(),
        "Results" => media_analysis_results(),
        "Status" => list(any())
      }
      
  """
  @type media_analysis_job_description() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_text_detection_response() :: %{
        "JobId" => String.t() | atom()
      }
      
  """
  @type start_text_detection_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      label_detection_settings() :: %{
        "GeneralLabels" => general_labels_settings()
      }
      
  """
  @type label_detection_settings() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      stream_processor_input() :: %{
        "KinesisVideoStream" => kinesis_video_stream()
      }
      
  """
  @type stream_processor_input() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_person_tracking_response() :: %{
        "JobId" => String.t() | atom(),
        "JobStatus" => list(any()),
        "JobTag" => String.t() | atom(),
        "NextToken" => String.t() | atom(),
        "Persons" => list(person_detection()),
        "StatusMessage" => String.t() | atom(),
        "Video" => video(),
        "VideoMetadata" => video_metadata()
      }
      
  """
  @type get_person_tracking_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      copy_project_version_response() :: %{
        "ProjectVersionArn" => String.t() | atom()
      }
      
  """
  @type copy_project_version_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      image() :: %{
        "Bytes" => binary(),
        "S3Object" => s3_object()
      }
      
  """
  @type image() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_media_analysis_jobs_request() :: %{
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom()
      }
      
  """
  @type list_media_analysis_jobs_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_content_moderation_request() :: %{
        optional("ClientRequestToken") => String.t() | atom(),
        optional("JobTag") => String.t() | atom(),
        optional("MinConfidence") => float(),
        optional("NotificationChannel") => notification_channel(),
        required("Video") => video()
      }
      
  """
  @type start_content_moderation_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      covers_body_part() :: %{
        "Confidence" => float(),
        "Value" => boolean()
      }
      
  """
  @type covers_body_part() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      internal_server_error() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type internal_server_error() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      instance() :: %{
        "BoundingBox" => bounding_box(),
        "Confidence" => float(),
        "DominantColors" => list(dominant_color())
      }
      
  """
  @type instance() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      image_quality() :: %{
        "Brightness" => float(),
        "Sharpness" => float()
      }
      
  """
  @type image_quality() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_faces_response() :: %{
        "FaceModelVersion" => String.t() | atom(),
        "Faces" => list(face()),
        "NextToken" => String.t() | atom()
      }
      
  """
  @type list_faces_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      access_denied_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type access_denied_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      delete_project_policy_request() :: %{
        optional("PolicyRevisionId") => String.t() | atom(),
        required("PolicyName") => String.t() | atom(),
        required("ProjectArn") => String.t() | atom()
      }
      
  """
  @type delete_project_policy_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      invalid_parameter_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type invalid_parameter_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      create_stream_processor_request() :: %{
        optional("DataSharingPreference") => stream_processor_data_sharing_preference(),
        optional("KmsKeyId") => String.t() | atom(),
        optional("NotificationChannel") => stream_processor_notification_channel(),
        optional("RegionsOfInterest") => list(region_of_interest()),
        optional("Tags") => map(),
        required("Input") => stream_processor_input(),
        required("Name") => String.t() | atom(),
        required("Output") => stream_processor_output(),
        required("RoleArn") => String.t() | atom(),
        required("Settings") => stream_processor_settings()
      }
      
  """
  @type create_stream_processor_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      index_faces_response() :: %{
        "FaceModelVersion" => String.t() | atom(),
        "FaceRecords" => list(face_record()),
        "OrientationCorrection" => list(any()),
        "UnindexedFaces" => list(unindexed_face())
      }
      
  """
  @type index_faces_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      tag_resource_response() :: %{}
      
  """
  @type tag_resource_response() :: %{}

  @typedoc """

  ## Example:
      
      get_media_analysis_job_request() :: %{
        required("JobId") => String.t() | atom()
      }
      
  """
  @type get_media_analysis_job_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      technical_cue_segment() :: %{
        "Confidence" => float(),
        "Type" => list(any())
      }
      
  """
  @type technical_cue_segment() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      output_config() :: %{
        "S3Bucket" => String.t() | atom(),
        "S3KeyPrefix" => String.t() | atom()
      }
      
  """
  @type output_config() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      video_metadata() :: %{
        "Codec" => String.t() | atom(),
        "ColorRange" => list(any()),
        "DurationMillis" => float(),
        "Format" => String.t() | atom(),
        "FrameHeight" => float(),
        "FrameRate" => float(),
        "FrameWidth" => float()
      }
      
  """
  @type video_metadata() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_moderation_labels_request() :: %{
        optional("HumanLoopConfig") => human_loop_config(),
        optional("MinConfidence") => float(),
        optional("ProjectVersion") => String.t() | atom(),
        required("Image") => image()
      }
      
  """
  @type detect_moderation_labels_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      eyeglasses() :: %{
        "Confidence" => float(),
        "Value" => boolean()
      }
      
  """
  @type eyeglasses() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_content_moderation_response() :: %{
        "JobId" => String.t() | atom()
      }
      
  """
  @type start_content_moderation_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_segment_detection_response() :: %{
        "JobId" => String.t() | atom()
      }
      
  """
  @type start_segment_detection_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      unsuccessful_face_association() :: %{
        "Confidence" => float(),
        "FaceId" => String.t() | atom(),
        "Reasons" => list(list(any())()),
        "UserId" => String.t() | atom()
      }
      
  """
  @type unsuccessful_face_association() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_faces_request() :: %{
        optional("Attributes") => list(list(any())()),
        required("Image") => image()
      }
      
  """
  @type detect_faces_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      describe_dataset_response() :: %{
        "DatasetDescription" => dataset_description()
      }
      
  """
  @type describe_dataset_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      person_detection() :: %{
        "Person" => person_detail(),
        "Timestamp" => float()
      }
      
  """
  @type person_detection() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_person_tracking_request() :: %{
        optional("ClientRequestToken") => String.t() | atom(),
        optional("JobTag") => String.t() | atom(),
        optional("NotificationChannel") => notification_channel(),
        required("Video") => video()
      }
      
  """
  @type start_person_tracking_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      distribute_dataset() :: %{
        "Arn" => String.t() | atom()
      }
      
  """
  @type distribute_dataset() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_tags_for_resource_request() :: %{
        required("ResourceArn") => String.t() | atom()
      }
      
  """
  @type list_tags_for_resource_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      invalid_image_format_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type invalid_image_format_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      create_dataset_request() :: %{
        optional("DatasetSource") => dataset_source(),
        optional("Tags") => map(),
        required("DatasetType") => list(any()),
        required("ProjectArn") => String.t() | atom()
      }
      
  """
  @type create_dataset_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      face_detection() :: %{
        "Face" => face_detail(),
        "Timestamp" => float()
      }
      
  """
  @type face_detection() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      audit_image() :: %{
        "BoundingBox" => bounding_box(),
        "Bytes" => binary(),
        "S3Object" => s3_object()
      }
      
  """
  @type audit_image() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_celebrity_recognition_response() :: %{
        "JobId" => String.t() | atom()
      }
      
  """
  @type start_celebrity_recognition_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_segment_detection_request() :: %{
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom(),
        required("JobId") => String.t() | atom()
      }
      
  """
  @type get_segment_detection_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      shot_segment() :: %{
        "Confidence" => float(),
        "Index" => float()
      }
      
  """
  @type shot_segment() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_face_liveness_session_results_response() :: %{
        "AuditImages" => list(audit_image()),
        "Challenge" => challenge(),
        "Confidence" => float(),
        "ReferenceImage" => audit_image(),
        "SessionId" => String.t() | atom(),
        "Status" => list(any())
      }
      
  """
  @type get_face_liveness_session_results_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      throttling_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type throttling_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      delete_stream_processor_response() :: %{}
      
  """
  @type delete_stream_processor_response() :: %{}

  @typedoc """

  ## Example:
      
      disassociated_face() :: %{
        "FaceId" => String.t() | atom()
      }
      
  """
  @type disassociated_face() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      user() :: %{
        "UserId" => String.t() | atom(),
        "UserStatus" => list(any())
      }
      
  """
  @type user() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      text_detection() :: %{
        "Confidence" => float(),
        "DetectedText" => String.t() | atom(),
        "Geometry" => geometry(),
        "Id" => integer(),
        "ParentId" => integer(),
        "Type" => list(any())
      }
      
  """
  @type text_detection() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      text_detection_result() :: %{
        "TextDetection" => text_detection(),
        "Timestamp" => float()
      }
      
  """
  @type text_detection_result() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      create_project_request() :: %{
        optional("AutoUpdate") => list(any()),
        optional("Feature") => list(any()),
        optional("Tags") => map(),
        required("ProjectName") => String.t() | atom()
      }
      
  """
  @type create_project_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      malformed_policy_document_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type malformed_policy_document_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      session_not_found_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type session_not_found_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      customization_feature_config() :: %{
        "ContentModeration" => customization_feature_content_moderation_config()
      }
      
  """
  @type customization_feature_config() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      describe_stream_processor_request() :: %{
        required("Name") => String.t() | atom()
      }
      
  """
  @type describe_stream_processor_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      pose() :: %{
        "Pitch" => float(),
        "Roll" => float(),
        "Yaw" => float()
      }
      
  """
  @type pose() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      update_stream_processor_request() :: %{
        optional("DataSharingPreferenceForUpdate") => stream_processor_data_sharing_preference(),
        optional("ParametersToDelete") => list(list(any())()),
        optional("RegionsOfInterestForUpdate") => list(region_of_interest()),
        optional("SettingsForUpdate") => stream_processor_settings_for_update(),
        required("Name") => String.t() | atom()
      }
      
  """
  @type update_stream_processor_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_stream_processor_request() :: %{
        optional("StartSelector") => stream_processing_start_selector(),
        optional("StopSelector") => stream_processing_stop_selector(),
        required("Name") => String.t() | atom()
      }
      
  """
  @type start_stream_processor_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      copy_project_version_request() :: %{
        optional("KmsKeyId") => String.t() | atom(),
        optional("Tags") => map(),
        required("DestinationProjectArn") => String.t() | atom(),
        required("OutputConfig") => output_config(),
        required("SourceProjectArn") => String.t() | atom(),
        required("SourceProjectVersionArn") => String.t() | atom(),
        required("VersionName") => String.t() | atom()
      }
      
  """
  @type copy_project_version_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      equipment_detection() :: %{
        "BoundingBox" => bounding_box(),
        "Confidence" => float(),
        "CoversBodyPart" => covers_body_part(),
        "Type" => list(any())
      }
      
  """
  @type equipment_detection() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_users_request() :: %{
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom(),
        required("CollectionId") => String.t() | atom()
      }
      
  """
  @type list_users_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      general_labels_settings() :: %{
        "LabelCategoryExclusionFilters" => list(String.t() | atom()),
        "LabelCategoryInclusionFilters" => list(String.t() | atom()),
        "LabelExclusionFilters" => list(String.t() | atom()),
        "LabelInclusionFilters" => list(String.t() | atom())
      }
      
  """
  @type general_labels_settings() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      media_analysis_input() :: %{
        "S3Object" => s3_object()
      }
      
  """
  @type media_analysis_input() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      stream_processor_notification_channel() :: %{
        "SNSTopicArn" => String.t() | atom()
      }
      
  """
  @type stream_processor_notification_channel() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      limit_exceeded_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type limit_exceeded_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_media_analysis_job_response() :: %{
        "JobId" => String.t() | atom()
      }
      
  """
  @type start_media_analysis_job_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      media_analysis_detect_moderation_labels_config() :: %{
        "MinConfidence" => float(),
        "ProjectVersion" => String.t() | atom()
      }
      
  """
  @type media_analysis_detect_moderation_labels_config() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      invalid_manifest_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type invalid_manifest_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      video_too_large_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type video_too_large_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      human_loop_quota_exceeded_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom(),
        "QuotaCode" => String.t() | atom(),
        "ResourceType" => String.t() | atom(),
        "ServiceCode" => String.t() | atom()
      }
      
  """
  @type human_loop_quota_exceeded_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      disassociate_faces_request() :: %{
        optional("ClientRequestToken") => String.t() | atom(),
        required("CollectionId") => String.t() | atom(),
        required("FaceIds") => list(String.t() | atom()),
        required("UserId") => String.t() | atom()
      }
      
  """
  @type disassociate_faces_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_text_detection_response() :: %{
        "JobId" => String.t() | atom(),
        "JobStatus" => list(any()),
        "JobTag" => String.t() | atom(),
        "NextToken" => String.t() | atom(),
        "StatusMessage" => String.t() | atom(),
        "TextDetections" => list(text_detection_result()),
        "TextModelVersion" => String.t() | atom(),
        "Video" => video(),
        "VideoMetadata" => video_metadata()
      }
      
  """
  @type get_text_detection_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_celebrity_info_request() :: %{
        required("Id") => String.t() | atom()
      }
      
  """
  @type get_celebrity_info_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      protective_equipment_summary() :: %{
        "PersonsIndeterminate" => list(integer()),
        "PersonsWithRequiredEquipment" => list(integer()),
        "PersonsWithoutRequiredEquipment" => list(integer())
      }
      
  """
  @type protective_equipment_summary() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_text_detection_filters() :: %{
        "RegionsOfInterest" => list(region_of_interest()),
        "WordFilter" => detection_filter()
      }
      
  """
  @type start_text_detection_filters() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_segment_detection_request() :: %{
        optional("ClientRequestToken") => String.t() | atom(),
        optional("Filters") => start_segment_detection_filters(),
        optional("JobTag") => String.t() | atom(),
        optional("NotificationChannel") => notification_channel(),
        required("SegmentTypes") => list(list(any())()),
        required("Video") => video()
      }
      
  """
  @type start_segment_detection_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      evaluation_result() :: %{
        "F1Score" => float(),
        "Summary" => summary()
      }
      
  """
  @type evaluation_result() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      user_match() :: %{
        "Similarity" => float(),
        "User" => matched_user()
      }
      
  """
  @type user_match() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      describe_dataset_request() :: %{
        required("DatasetArn") => String.t() | atom()
      }
      
  """
  @type describe_dataset_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      delete_dataset_request() :: %{
        required("DatasetArn") => String.t() | atom()
      }
      
  """
  @type delete_dataset_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_labels_image_quality() :: %{
        "Brightness" => float(),
        "Contrast" => float(),
        "Sharpness" => float()
      }
      
  """
  @type detect_labels_image_quality() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      describe_project_versions_response() :: %{
        "NextToken" => String.t() | atom(),
        "ProjectVersionDescriptions" => list(project_version_description())
      }
      
  """
  @type describe_project_versions_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_labels_settings() :: %{
        "GeneralLabels" => general_labels_settings(),
        "ImageProperties" => detect_labels_image_properties_settings()
      }
      
  """
  @type detect_labels_settings() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      liveness_output_config() :: %{
        "S3Bucket" => String.t() | atom(),
        "S3KeyPrefix" => String.t() | atom()
      }
      
  """
  @type liveness_output_config() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      face_record() :: %{
        "Face" => face(),
        "FaceDetail" => face_detail()
      }
      
  """
  @type face_record() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_label_detection_response() :: %{
        "JobId" => String.t() | atom()
      }
      
  """
  @type start_label_detection_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      parent() :: %{
        "Name" => String.t() | atom()
      }
      
  """
  @type parent() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      resource_already_exists_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type resource_already_exists_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      validation_data() :: %{
        "Assets" => list(asset())
      }
      
  """
  @type validation_data() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      describe_project_versions_request() :: %{
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom(),
        optional("VersionNames") => list(String.t() | atom()),
        required("ProjectArn") => String.t() | atom()
      }
      
  """
  @type describe_project_versions_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_labels_image_foreground() :: %{
        "DominantColors" => list(dominant_color()),
        "Quality" => detect_labels_image_quality()
      }
      
  """
  @type detect_labels_image_foreground() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      invalid_s3_object_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type invalid_s3_object_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      media_analysis_model_versions() :: %{
        "Moderation" => String.t() | atom()
      }
      
  """
  @type media_analysis_model_versions() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      delete_project_request() :: %{
        required("ProjectArn") => String.t() | atom()
      }
      
  """
  @type delete_project_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      kinesis_data_stream() :: %{
        "Arn" => String.t() | atom()
      }
      
  """
  @type kinesis_data_stream() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      label_alias() :: %{
        "Name" => String.t() | atom()
      }
      
  """
  @type label_alias() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      mustache() :: %{
        "Confidence" => float(),
        "Value" => boolean()
      }
      
  """
  @type mustache() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      sunglasses() :: %{
        "Confidence" => float(),
        "Value" => boolean()
      }
      
  """
  @type sunglasses() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      human_loop_config() :: %{
        "DataAttributes" => human_loop_data_attributes(),
        "FlowDefinitionArn" => String.t() | atom(),
        "HumanLoopName" => String.t() | atom()
      }
      
  """
  @type human_loop_config() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_face_search_request() :: %{
        optional("ClientRequestToken") => String.t() | atom(),
        optional("FaceMatchThreshold") => float(),
        optional("JobTag") => String.t() | atom(),
        optional("NotificationChannel") => notification_channel(),
        required("CollectionId") => String.t() | atom(),
        required("Video") => video()
      }
      
  """
  @type start_face_search_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      resource_not_ready_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type resource_not_ready_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      delete_collection_response() :: %{
        "StatusCode" => integer()
      }
      
  """
  @type delete_collection_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_content_moderation_response() :: %{
        "GetRequestMetadata" => get_content_moderation_request_metadata(),
        "JobId" => String.t() | atom(),
        "JobStatus" => list(any()),
        "JobTag" => String.t() | atom(),
        "ModerationLabels" => list(content_moderation_detection()),
        "ModerationModelVersion" => String.t() | atom(),
        "NextToken" => String.t() | atom(),
        "StatusMessage" => String.t() | atom(),
        "Video" => video(),
        "VideoMetadata" => video_metadata()
      }
      
  """
  @type get_content_moderation_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      create_user_response() :: %{}
      
  """
  @type create_user_response() :: %{}

  @typedoc """

  ## Example:
      
      compare_faces_response() :: %{
        "FaceMatches" => list(compare_faces_match()),
        "SourceImageFace" => compared_source_image_face(),
        "SourceImageOrientationCorrection" => list(any()),
        "TargetImageOrientationCorrection" => list(any()),
        "UnmatchedFaces" => list(compared_face())
      }
      
  """
  @type compare_faces_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      project_policy() :: %{
        "CreationTimestamp" => non_neg_integer(),
        "LastUpdatedTimestamp" => non_neg_integer(),
        "PolicyDocument" => String.t() | atom(),
        "PolicyName" => String.t() | atom(),
        "PolicyRevisionId" => String.t() | atom(),
        "ProjectArn" => String.t() | atom()
      }
      
  """
  @type project_policy() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      search_faces_by_image_response() :: %{
        "FaceMatches" => list(face_match()),
        "FaceModelVersion" => String.t() | atom(),
        "SearchedFaceBoundingBox" => bounding_box(),
        "SearchedFaceConfidence" => float()
      }
      
  """
  @type search_faces_by_image_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      list_dataset_entries_response() :: %{
        "DatasetEntries" => list(String.t() | atom()),
        "NextToken" => String.t() | atom()
      }
      
  """
  @type list_dataset_entries_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      summary() :: %{
        "S3Object" => s3_object()
      }
      
  """
  @type summary() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      kinesis_video_stream() :: %{
        "Arn" => String.t() | atom()
      }
      
  """
  @type kinesis_video_stream() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      dataset_metadata() :: %{
        "CreationTimestamp" => non_neg_integer(),
        "DatasetArn" => String.t() | atom(),
        "DatasetType" => list(any()),
        "Status" => list(any()),
        "StatusMessage" => String.t() | atom(),
        "StatusMessageCode" => list(any())
      }
      
  """
  @type dataset_metadata() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      detect_labels_image_background() :: %{
        "DominantColors" => list(dominant_color()),
        "Quality" => detect_labels_image_quality()
      }
      
  """
  @type detect_labels_image_background() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      celebrity_detail() :: %{
        "BoundingBox" => bounding_box(),
        "Confidence" => float(),
        "Face" => face_detail(),
        "Id" => String.t() | atom(),
        "KnownGender" => known_gender(),
        "Name" => String.t() | atom(),
        "Urls" => list(String.t() | atom())
      }
      
  """
  @type celebrity_detail() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      stream_processing_stop_selector() :: %{
        "MaxDurationInSeconds" => float()
      }
      
  """
  @type stream_processing_stop_selector() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      audio_metadata() :: %{
        "Codec" => String.t() | atom(),
        "DurationMillis" => float(),
        "NumberOfChannels" => float(),
        "SampleRate" => float()
      }
      
  """
  @type audio_metadata() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      gender() :: %{
        "Confidence" => float(),
        "Value" => list(any())
      }
      
  """
  @type gender() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      label() :: %{
        "Aliases" => list(label_alias()),
        "Categories" => list(label_category()),
        "Confidence" => float(),
        "Instances" => list(instance()),
        "Name" => String.t() | atom(),
        "Parents" => list(parent())
      }
      
  """
  @type label() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_face_search_response() :: %{
        "JobId" => String.t() | atom(),
        "JobStatus" => list(any()),
        "JobTag" => String.t() | atom(),
        "NextToken" => String.t() | atom(),
        "Persons" => list(person_match()),
        "StatusMessage" => String.t() | atom(),
        "Video" => video(),
        "VideoMetadata" => video_metadata()
      }
      
  """
  @type get_face_search_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      start_project_version_request() :: %{
        optional("MaxInferenceUnits") => integer(),
        required("MinInferenceUnits") => integer(),
        required("ProjectVersionArn") => String.t() | atom()
      }
      
  """
  @type start_project_version_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      compared_face() :: %{
        "BoundingBox" => bounding_box(),
        "Confidence" => float(),
        "Emotions" => list(emotion()),
        "Landmarks" => list(landmark()),
        "Pose" => pose(),
        "Quality" => image_quality(),
        "Smile" => smile()
      }
      
  """
  @type compared_face() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_face_detection_request() :: %{
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom(),
        required("JobId") => String.t() | atom()
      }
      
  """
  @type get_face_detection_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      delete_project_version_request() :: %{
        required("ProjectVersionArn") => String.t() | atom()
      }
      
  """
  @type delete_project_version_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      stop_project_version_response() :: %{
        "Status" => list(any())
      }
      
  """
  @type stop_project_version_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      create_user_request() :: %{
        optional("ClientRequestToken") => String.t() | atom(),
        required("CollectionId") => String.t() | atom(),
        required("UserId") => String.t() | atom()
      }
      
  """
  @type create_user_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      create_face_liveness_session_request() :: %{
        optional("ClientRequestToken") => String.t() | atom(),
        optional("KmsKeyId") => String.t() | atom(),
        optional("Settings") => create_face_liveness_session_request_settings()
      }
      
  """
  @type create_face_liveness_session_request() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      invalid_pagination_token_exception() :: %{
        "Code" => String.t() | atom(),
        "Logref" => String.t() | atom(),
        "Message" => String.t() | atom()
      }
      
  """
  @type invalid_pagination_token_exception() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      delete_project_version_response() :: %{
        "Status" => list(any())
      }
      
  """
  @type delete_project_version_response() :: %{(String.t() | atom()) => any()}

  @typedoc """

  ## Example:
      
      get_person_tracking_request() :: %{
        optional("MaxResults") => integer(),
        optional("NextToken") => String.t() | atom(),
        optional("SortBy") => list(any()),
        required("JobId") => String.t() | atom()
      }
      
  """
  @type get_person_tracking_request() :: %{(String.t() | atom()) => any()}

  @type associate_faces_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | idempotent_parameter_mismatch_exception()
          | service_quota_exceeded_exception()
          | resource_not_found_exception()
          | conflict_exception()
          | provisioned_throughput_exceeded_exception()

  @type compare_faces_errors() ::
          invalid_s3_object_exception()
          | throttling_exception()
          | invalid_image_format_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | provisioned_throughput_exceeded_exception()
          | image_too_large_exception()

  @type copy_project_version_errors() ::
          limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | service_quota_exceeded_exception()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | resource_in_use_exception()

  @type create_collection_errors() ::
          resource_already_exists_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | service_quota_exceeded_exception()
          | provisioned_throughput_exceeded_exception()

  @type create_dataset_errors() ::
          invalid_s3_object_exception()
          | resource_already_exists_exception()
          | limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type create_face_liveness_session_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | provisioned_throughput_exceeded_exception()

  @type create_project_errors() ::
          limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | provisioned_throughput_exceeded_exception()
          | resource_in_use_exception()

  @type create_project_version_errors() ::
          limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | service_quota_exceeded_exception()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | resource_in_use_exception()

  @type create_stream_processor_errors() ::
          limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | service_quota_exceeded_exception()
          | provisioned_throughput_exceeded_exception()
          | resource_in_use_exception()

  @type create_user_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | idempotent_parameter_mismatch_exception()
          | service_quota_exceeded_exception()
          | resource_not_found_exception()
          | conflict_exception()
          | provisioned_throughput_exceeded_exception()

  @type delete_collection_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type delete_dataset_errors() ::
          limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | resource_in_use_exception()

  @type delete_faces_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type delete_project_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | resource_in_use_exception()

  @type delete_project_policy_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | invalid_policy_revision_id_exception()
          | provisioned_throughput_exceeded_exception()

  @type delete_project_version_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | resource_in_use_exception()

  @type delete_stream_processor_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | resource_in_use_exception()

  @type delete_user_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | idempotent_parameter_mismatch_exception()
          | resource_not_found_exception()
          | conflict_exception()
          | provisioned_throughput_exceeded_exception()

  @type describe_collection_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type describe_dataset_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type describe_project_versions_errors() ::
          invalid_pagination_token_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type describe_projects_errors() ::
          invalid_pagination_token_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | provisioned_throughput_exceeded_exception()

  @type describe_stream_processor_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type detect_custom_labels_errors() ::
          resource_not_ready_exception()
          | invalid_s3_object_exception()
          | limit_exceeded_exception()
          | throttling_exception()
          | invalid_image_format_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | image_too_large_exception()

  @type detect_faces_errors() ::
          invalid_s3_object_exception()
          | throttling_exception()
          | invalid_image_format_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | provisioned_throughput_exceeded_exception()
          | image_too_large_exception()

  @type detect_labels_errors() ::
          invalid_s3_object_exception()
          | throttling_exception()
          | invalid_image_format_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | provisioned_throughput_exceeded_exception()
          | image_too_large_exception()

  @type detect_moderation_labels_errors() ::
          resource_not_ready_exception()
          | invalid_s3_object_exception()
          | human_loop_quota_exceeded_exception()
          | throttling_exception()
          | invalid_image_format_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | image_too_large_exception()

  @type detect_protective_equipment_errors() ::
          invalid_s3_object_exception()
          | throttling_exception()
          | invalid_image_format_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | provisioned_throughput_exceeded_exception()
          | image_too_large_exception()

  @type detect_text_errors() ::
          invalid_s3_object_exception()
          | throttling_exception()
          | invalid_image_format_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | provisioned_throughput_exceeded_exception()
          | image_too_large_exception()

  @type disassociate_faces_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | idempotent_parameter_mismatch_exception()
          | resource_not_found_exception()
          | conflict_exception()
          | provisioned_throughput_exceeded_exception()

  @type distribute_dataset_entries_errors() ::
          resource_not_ready_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type get_celebrity_info_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type get_celebrity_recognition_errors() ::
          invalid_pagination_token_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type get_content_moderation_errors() ::
          invalid_pagination_token_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type get_face_detection_errors() ::
          invalid_pagination_token_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type get_face_liveness_session_results_errors() ::
          session_not_found_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | provisioned_throughput_exceeded_exception()

  @type get_face_search_errors() ::
          invalid_pagination_token_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type get_label_detection_errors() ::
          invalid_pagination_token_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type get_media_analysis_job_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type get_person_tracking_errors() ::
          invalid_pagination_token_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type get_segment_detection_errors() ::
          invalid_pagination_token_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type get_text_detection_errors() ::
          invalid_pagination_token_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type index_faces_errors() ::
          invalid_s3_object_exception()
          | throttling_exception()
          | invalid_image_format_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | service_quota_exceeded_exception()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | image_too_large_exception()

  @type list_collections_errors() ::
          invalid_pagination_token_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type list_dataset_entries_errors() ::
          invalid_pagination_token_exception()
          | resource_not_ready_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | resource_in_use_exception()

  @type list_dataset_labels_errors() ::
          invalid_pagination_token_exception()
          | resource_not_ready_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | resource_in_use_exception()

  @type list_faces_errors() ::
          invalid_pagination_token_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type list_media_analysis_jobs_errors() ::
          invalid_pagination_token_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | provisioned_throughput_exceeded_exception()

  @type list_project_policies_errors() ::
          invalid_pagination_token_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type list_stream_processors_errors() ::
          invalid_pagination_token_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | provisioned_throughput_exceeded_exception()

  @type list_tags_for_resource_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type list_users_errors() ::
          invalid_pagination_token_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type put_project_policy_errors() ::
          resource_already_exists_exception()
          | limit_exceeded_exception()
          | malformed_policy_document_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | service_quota_exceeded_exception()
          | resource_not_found_exception()
          | invalid_policy_revision_id_exception()
          | provisioned_throughput_exceeded_exception()

  @type recognize_celebrities_errors() ::
          invalid_s3_object_exception()
          | throttling_exception()
          | invalid_image_format_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | provisioned_throughput_exceeded_exception()
          | image_too_large_exception()

  @type search_faces_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type search_faces_by_image_errors() ::
          invalid_s3_object_exception()
          | throttling_exception()
          | invalid_image_format_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | image_too_large_exception()

  @type search_users_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type search_users_by_image_errors() ::
          invalid_s3_object_exception()
          | throttling_exception()
          | invalid_image_format_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | image_too_large_exception()

  @type start_celebrity_recognition_errors() ::
          invalid_s3_object_exception()
          | video_too_large_exception()
          | limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | idempotent_parameter_mismatch_exception()
          | provisioned_throughput_exceeded_exception()

  @type start_content_moderation_errors() ::
          invalid_s3_object_exception()
          | video_too_large_exception()
          | limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | idempotent_parameter_mismatch_exception()
          | provisioned_throughput_exceeded_exception()

  @type start_face_detection_errors() ::
          invalid_s3_object_exception()
          | video_too_large_exception()
          | limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | idempotent_parameter_mismatch_exception()
          | provisioned_throughput_exceeded_exception()

  @type start_face_search_errors() ::
          invalid_s3_object_exception()
          | video_too_large_exception()
          | limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | idempotent_parameter_mismatch_exception()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type start_label_detection_errors() ::
          invalid_s3_object_exception()
          | video_too_large_exception()
          | limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | idempotent_parameter_mismatch_exception()
          | provisioned_throughput_exceeded_exception()

  @type start_media_analysis_job_errors() ::
          resource_not_ready_exception()
          | invalid_s3_object_exception()
          | invalid_manifest_exception()
          | limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | idempotent_parameter_mismatch_exception()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type start_person_tracking_errors() ::
          invalid_s3_object_exception()
          | video_too_large_exception()
          | limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | idempotent_parameter_mismatch_exception()
          | provisioned_throughput_exceeded_exception()

  @type start_project_version_errors() ::
          limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | resource_in_use_exception()

  @type start_segment_detection_errors() ::
          invalid_s3_object_exception()
          | video_too_large_exception()
          | limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | idempotent_parameter_mismatch_exception()
          | provisioned_throughput_exceeded_exception()

  @type start_stream_processor_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | resource_in_use_exception()

  @type start_text_detection_errors() ::
          invalid_s3_object_exception()
          | video_too_large_exception()
          | limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | idempotent_parameter_mismatch_exception()
          | provisioned_throughput_exceeded_exception()

  @type stop_project_version_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | resource_in_use_exception()

  @type stop_stream_processor_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | resource_in_use_exception()

  @type tag_resource_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | service_quota_exceeded_exception()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type untag_resource_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()

  @type update_dataset_entries_errors() ::
          limit_exceeded_exception()
          | throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | resource_in_use_exception()

  @type update_stream_processor_errors() ::
          throttling_exception()
          | invalid_parameter_exception()
          | access_denied_exception()
          | internal_server_error()
          | resource_not_found_exception()
          | provisioned_throughput_exceeded_exception()
          | resource_in_use_exception()

  def metadata do
    %{
      api_version: "2016-06-27",
      content_type: "application/x-amz-json-1.1",
      credential_scope: nil,
      endpoint_prefix: "rekognition",
      global?: false,
      hostname: nil,
      protocol: "json",
      service_id: "Rekognition",
      signature_version: "v4",
      signing_name: "rekognition",
      target_prefix: "RekognitionService"
    }
  end

  @doc """
  Associates one or more faces with an existing UserID.

  Takes an array of
  `FaceIds`. Each `FaceId` that are present in the `FaceIds`
  list is associated with the provided UserID. The number of FaceIds that can be
  used as input
  in a single request is limited to 100.

  Note that the total number of faces that can be associated with a single
  `UserID` is also limited to 100. Once a `UserID` has 100 faces
  associated with it, no additional faces can be added. If more API calls are made
  after the
  limit is reached, a `ServiceQuotaExceededException` will result.

  The `UserMatchThreshold` parameter specifies the minimum user match confidence
  required for the face to be associated with a UserID that has at least one
  `FaceID`
  already associated. This ensures that the `FaceIds` are associated with the
  right
  UserID. The value ranges from 0-100 and default value is 75.

  If successful, an array of `AssociatedFace` objects containing the associated
  `FaceIds` is returned. If a given face is already associated with the given
  `UserID`, it will be ignored and will not be returned in the response. If a
  given
  face is already associated to a different `UserID`, isn't found in the
  collection,
  doesn’t meet the `UserMatchThreshold`, or there are already 100 faces associated
  with the `UserID`, it will be returned as part of an array of
  `UnsuccessfulFaceAssociations.`

  The `UserStatus` reflects the status of an operation which updates a UserID
  representation with a list of given faces. The `UserStatus` can be:

    *
  ACTIVE - All associations or disassociations of FaceID(s) for a UserID are
  complete.

    *
  CREATED - A UserID has been created, but has no FaceID(s) associated with it.

    *
  UPDATING - A UserID is being updated and there are current associations or
  disassociations of FaceID(s) taking place.
  """
  @spec associate_faces(map(), associate_faces_request(), list()) ::
          {:ok, associate_faces_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, associate_faces_errors()}
  def associate_faces(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "AssociateFaces", input, options)
  end

  @doc """
  Compares a face in the *source* input image with each of the 100
  largest faces detected in the *target* input image.

  If the source image contains multiple faces, the service detects the largest
  face and
  compares it with each face detected in the target image.

  CompareFaces uses machine learning algorithms, which are probabilistic. A false
  negative
  is an incorrect prediction that a face in the target image has a low similarity
  confidence
  score when compared to the face in the source image. To reduce the probability
  of false
  negatives, we recommend that you compare the target image against multiple
  source images. If
  you plan to use `CompareFaces` to make a decision that impacts an individual's
  rights, privacy, or access to services, we recommend that you pass the result to
  a human for
  review and further validation before taking action.

  You pass the input and target images either as base64-encoded image bytes or as
  references to images in an Amazon S3 bucket. If you use the
  AWS
  CLI to call Amazon Rekognition operations, passing image bytes isn't
  supported. The image must be formatted as a PNG or JPEG file.

  In response, the operation returns an array of face matches ordered by
  similarity score
  in descending order. For each face match, the response provides a bounding box
  of the face,
  facial landmarks, pose details (pitch, roll, and yaw), quality (brightness and
  sharpness), and
  confidence value (indicating the level of confidence that the bounding box
  contains a face).
  The response also provides a similarity score, which indicates how closely the
  faces match.

  By default, only faces with a similarity score of greater than or equal to 80%
  are
  returned in the response. You can change this value by specifying the
  `SimilarityThreshold` parameter.

  `CompareFaces` also returns an array of faces that don't match the source
  image. For each face, it returns a bounding box, confidence value, landmarks,
  pose details,
  and quality. The response also returns information about the face in the source
  image,
  including the bounding box of the face and confidence value.

  The `QualityFilter` input parameter allows you to filter out detected faces
  that don’t meet a required quality bar. The quality bar is based on a variety of
  common use
  cases. Use `QualityFilter` to set the quality bar by specifying `LOW`,
  `MEDIUM`, or `HIGH`. If you do not want to filter detected faces,
  specify `NONE`. The default value is `NONE`.

  If the image doesn't contain Exif metadata, `CompareFaces` returns
  orientation information for the source and target images. Use these values to
  display the
  images with the correct image orientation.

  If no faces are detected in the source or target images, `CompareFaces`
  returns an `InvalidParameterException` error.

  This is a stateless API operation. That is, data returned by this operation
  doesn't
  persist.

  For an example, see Comparing Faces in Images in the Amazon Rekognition
  Developer
  Guide.

  This operation requires permissions to perform the
  `rekognition:CompareFaces` action.
  """
  @spec compare_faces(map(), compare_faces_request(), list()) ::
          {:ok, compare_faces_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, compare_faces_errors()}
  def compare_faces(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "CompareFaces", input, options)
  end

  @doc """

  This operation applies only to Amazon Rekognition Custom Labels.

  Copies a version of an Amazon Rekognition Custom Labels model from a source
  project to a destination project. The source and
  destination projects can be in different AWS accounts but must be in the same
  AWS Region.
  You can't copy a model to another AWS service.

  To copy a model version to a different AWS account, you need to create a
  resource-based policy known as a
  *project policy*. You attach the project policy to the
  source project by calling `PutProjectPolicy`. The project policy
  gives permission to copy the model version from a trusting AWS account to a
  trusted account.

  For more information creating and attaching a project policy, see Attaching a
  project policy (SDK)
  in the *Amazon Rekognition Custom Labels Developer Guide*.

  If you are copying a model version to a project in the same AWS account, you
  don't need to create a project policy.

  Copying project versions is supported only for Custom Labels models.

  To copy a model, the destination project, source project, and source model
  version
  must already exist.

  Copying a model version takes a while to complete. To get the current status,
  call `DescribeProjectVersions` and check the value of `Status` in the
  `ProjectVersionDescription` object. The copy operation has finished when
  the value of `Status` is `COPYING_COMPLETED`.

  This operation requires permissions to perform the
  `rekognition:CopyProjectVersion` action.
  """
  @spec copy_project_version(map(), copy_project_version_request(), list()) ::
          {:ok, copy_project_version_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, copy_project_version_errors()}
  def copy_project_version(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "CopyProjectVersion", input, options)
  end

  @doc """
  Creates a collection in an AWS Region.

  You can add faces to the collection using the
  `IndexFaces` operation.

  For example, you might create collections, one for each of your application
  users. A
  user can then index faces using the `IndexFaces` operation and persist results
  in a
  specific collection. Then, a user can search the collection for faces in the
  user-specific
  container.

  When you create a collection, it is associated with the latest version of the
  face model
  version.

  Collection names are case-sensitive.

  This operation requires permissions to perform the
  `rekognition:CreateCollection` action. If you want to tag your collection, you
  also require permission to perform the `rekognition:TagResource`
  operation.
  """
  @spec create_collection(map(), create_collection_request(), list()) ::
          {:ok, create_collection_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, create_collection_errors()}
  def create_collection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "CreateCollection", input, options)
  end

  @doc """

  This operation applies only to Amazon Rekognition Custom Labels.

  Creates a new Amazon Rekognition Custom Labels dataset. You can create a dataset
  by using
  an Amazon Sagemaker format manifest file or by copying an existing Amazon
  Rekognition Custom Labels dataset.

  To create a training dataset for a project, specify `TRAIN` for the value of
  `DatasetType`. To create the test dataset for a project,
  specify `TEST` for the value of `DatasetType`.

  The response from `CreateDataset` is the Amazon Resource Name (ARN) for the
  dataset.
  Creating a dataset takes a while to complete. Use `DescribeDataset` to check the
  current status. The dataset created successfully if the value of `Status` is
  `CREATE_COMPLETE`.

  To check if any non-terminal errors occurred, call `ListDatasetEntries`
  and check for the presence of `errors` lists in the JSON Lines.

  Dataset creation fails if a terminal error occurs (`Status` = `CREATE_FAILED`).
  Currently, you can't access the terminal error information.

  For more information, see Creating dataset in the *Amazon Rekognition Custom
  Labels Developer Guide*.

  This operation requires permissions to perform the `rekognition:CreateDataset`
  action.
  If you want to copy an existing dataset, you also require permission to perform
  the `rekognition:ListDatasetEntries` action.
  """
  @spec create_dataset(map(), create_dataset_request(), list()) ::
          {:ok, create_dataset_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, create_dataset_errors()}
  def create_dataset(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "CreateDataset", input, options)
  end

  @doc """
  This API operation initiates a Face Liveness session.

  It returns a `SessionId`,
  which you can use to start streaming Face Liveness video and get the results for
  a Face
  Liveness session.

  You can use the `OutputConfig` option in the Settings parameter to provide an
  Amazon S3 bucket location. The Amazon S3 bucket stores reference images and
  audit images. If no Amazon S3
  bucket is defined, raw bytes are sent instead.

  You can use `AuditImagesLimit` to limit the number of audit images returned
  when `GetFaceLivenessSessionResults` is called. This number is between 0 and 4.
  By
  default, it is set to 0. The limit is best effort and based on the duration of
  the
  selfie-video.
  """
  @spec create_face_liveness_session(map(), create_face_liveness_session_request(), list()) ::
          {:ok, create_face_liveness_session_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, create_face_liveness_session_errors()}
  def create_face_liveness_session(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "CreateFaceLivenessSession", input, options)
  end

  @doc """
  Creates a new Amazon Rekognition project.

  A project is a group of resources (datasets, model
  versions) that you use to create and manage a Amazon Rekognition Custom Labels
  Model or custom adapter. You can
  specify a feature to create the project with, if no feature is specified then
  Custom Labels
  is used by default. For adapters, you can also choose whether or not to have the
  project
  auto update by using the AutoUpdate argument. This operation requires
  permissions to
  perform the `rekognition:CreateProject` action.
  """
  @spec create_project(map(), create_project_request(), list()) ::
          {:ok, create_project_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, create_project_errors()}
  def create_project(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "CreateProject", input, options)
  end

  @doc """
  Creates a new version of Amazon Rekognition project (like a Custom Labels model
  or a custom adapter)
  and begins training.

  Models and adapters are managed as part of a Rekognition project. The
  response from `CreateProjectVersion` is an Amazon Resource Name (ARN) for the
  project version.

  The FeatureConfig operation argument allows you to configure specific model or
  adapter
  settings. You can provide a description to the project version by using the
  VersionDescription argment. Training can take a while to complete. You can get
  the current
  status by calling `DescribeProjectVersions`. Training completed
  successfully if the value of the `Status` field is
  `TRAINING_COMPLETED`. Once training has successfully completed, call
  `DescribeProjectVersions` to get the training results and evaluate the
  model.

  This operation requires permissions to perform the
  `rekognition:CreateProjectVersion` action.

  *The following applies only to projects with Amazon Rekognition Custom Labels as
  the chosen
  feature:*

  You can train a model in a project that doesn't have associated datasets by
  specifying manifest files in the
  `TrainingData` and `TestingData` fields.

  If you open the console after training a model with manifest files, Amazon
  Rekognition Custom Labels creates
  the datasets for you using the most recent manifest files. You can no longer
  train
  a model version for the project by specifying manifest files.

  Instead of training with a project without associated datasets,
  we recommend that you use the manifest
  files to create training and test datasets for the project.
  """
  @spec create_project_version(map(), create_project_version_request(), list()) ::
          {:ok, create_project_version_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, create_project_version_errors()}
  def create_project_version(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "CreateProjectVersion", input, options)
  end

  @doc """
  Creates an Amazon Rekognition stream processor that you can use to detect and
  recognize faces or to detect labels in a streaming video.

  Amazon Rekognition Video is a consumer of live video from Amazon Kinesis Video
  Streams. There are two different settings for stream processors in Amazon
  Rekognition: detecting faces and detecting labels.

    *
  If you are creating a stream processor for detecting faces, you provide as input
  a Kinesis video stream
  (`Input`) and a Kinesis data stream (`Output`) stream for receiving
  the output. You must use the `FaceSearch` option in
  `Settings`, specifying the collection that contains the faces you
  want to recognize. After you have finished analyzing a streaming video, use
  `StopStreamProcessor` to stop processing.

    *
  If you are creating a stream processor to detect labels, you provide as input a
  Kinesis video stream
  (`Input`), Amazon S3 bucket information (`Output`), and an
  Amazon SNS topic ARN (`NotificationChannel`). You can also provide a KMS
  key ID to encrypt the data sent to your Amazon S3 bucket. You specify what you
  want
  to detect by using the `ConnectedHome` option in settings, and
  selecting one of the following: `PERSON`, `PET`,
  `PACKAGE`, `ALL` You can also specify where in the
  frame you want Amazon Rekognition to monitor with `RegionsOfInterest`. When
  you run the `StartStreamProcessor` operation on a label
  detection stream processor, you input start and stop information to determine
  the length of the processing time.

  Use `Name` to assign an identifier for the stream processor. You use `Name`
  to manage the stream processor. For example, you can start processing the source
  video by calling `StartStreamProcessor` with
  the `Name` field.

  This operation requires permissions to perform the
  `rekognition:CreateStreamProcessor` action. If you want to tag your stream
  processor, you also require permission to perform the `rekognition:TagResource`
  operation.
  """
  @spec create_stream_processor(map(), create_stream_processor_request(), list()) ::
          {:ok, create_stream_processor_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, create_stream_processor_errors()}
  def create_stream_processor(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "CreateStreamProcessor", input, options)
  end

  @doc """
  Creates a new User within a collection specified by `CollectionId`.

  Takes
  `UserId` as a parameter, which is a user provided ID which should be unique
  within the collection. The provided `UserId` will alias the system generated
  UUID
  to make the `UserId` more user friendly.

  Uses a `ClientToken`, an idempotency token that ensures a call to
  `CreateUser` completes only once. If the value is not supplied, the AWS SDK
  generates an idempotency token for the requests. This prevents retries after a
  network error
  results from making multiple `CreateUser` calls.
  """
  @spec create_user(map(), create_user_request(), list()) ::
          {:ok, create_user_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, create_user_errors()}
  def create_user(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "CreateUser", input, options)
  end

  @doc """
  Deletes the specified collection.

  Note that this operation removes all faces in the
  collection. For an example, see [Deleting a collection](https://docs.aws.amazon.com/rekognition/latest/dg/delete-collection-procedure.html).

  This operation requires permissions to perform the
  `rekognition:DeleteCollection` action.
  """
  @spec delete_collection(map(), delete_collection_request(), list()) ::
          {:ok, delete_collection_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, delete_collection_errors()}
  def delete_collection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DeleteCollection", input, options)
  end

  @doc """

  This operation applies only to Amazon Rekognition Custom Labels.

  Deletes an existing Amazon Rekognition Custom Labels dataset.
  Deleting a dataset might take while. Use `DescribeDataset` to check the
  current status. The dataset is still deleting if the value of `Status` is
  `DELETE_IN_PROGRESS`. If you try to access the dataset after it is deleted, you
  get
  a `ResourceNotFoundException` exception.

  You can't delete a dataset while it is creating (`Status` =
  `CREATE_IN_PROGRESS`)
  or if the dataset is updating (`Status` = `UPDATE_IN_PROGRESS`).

  This operation requires permissions to perform the `rekognition:DeleteDataset`
  action.
  """
  @spec delete_dataset(map(), delete_dataset_request(), list()) ::
          {:ok, delete_dataset_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, delete_dataset_errors()}
  def delete_dataset(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DeleteDataset", input, options)
  end

  @doc """
  Deletes faces from a collection.

  You specify a collection ID and an array of face IDs
  to remove from the collection.

  This operation requires permissions to perform the `rekognition:DeleteFaces`
  action.
  """
  @spec delete_faces(map(), delete_faces_request(), list()) ::
          {:ok, delete_faces_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, delete_faces_errors()}
  def delete_faces(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DeleteFaces", input, options)
  end

  @doc """
  Deletes a Amazon Rekognition project.

  To delete a project you must first delete all models or
  adapters associated with the project. To delete a model or adapter, see
  `DeleteProjectVersion`.

  `DeleteProject` is an asynchronous operation. To check if the project is
  deleted, call `DescribeProjects`. The project is deleted when the project
  no longer appears in the response. Be aware that deleting a given project will
  also delete
  any `ProjectPolicies` associated with that project.

  This operation requires permissions to perform the
  `rekognition:DeleteProject` action.
  """
  @spec delete_project(map(), delete_project_request(), list()) ::
          {:ok, delete_project_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, delete_project_errors()}
  def delete_project(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DeleteProject", input, options)
  end

  @doc """

  This operation applies only to Amazon Rekognition Custom Labels.

  Deletes an existing project policy.

  To get a list of project policies attached to a project, call
  `ListProjectPolicies`. To attach a project policy to a project, call
  `PutProjectPolicy`.

  This operation requires permissions to perform the
  `rekognition:DeleteProjectPolicy` action.
  """
  @spec delete_project_policy(map(), delete_project_policy_request(), list()) ::
          {:ok, delete_project_policy_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, delete_project_policy_errors()}
  def delete_project_policy(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DeleteProjectPolicy", input, options)
  end

  @doc """
  Deletes a Rekognition project model or project version, like a Amazon
  Rekognition Custom Labels model or a custom
  adapter.

  You can't delete a project version if it is running or if it is training. To
  check
  the status of a project version, use the Status field returned from
  `DescribeProjectVersions`. To stop a project version call `StopProjectVersion`.
  If the project version is training, wait until it
  finishes.

  This operation requires permissions to perform the
  `rekognition:DeleteProjectVersion` action.
  """
  @spec delete_project_version(map(), delete_project_version_request(), list()) ::
          {:ok, delete_project_version_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, delete_project_version_errors()}
  def delete_project_version(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DeleteProjectVersion", input, options)
  end

  @doc """
  Deletes the stream processor identified by `Name`.

  You assign the value for `Name` when you create the stream processor with
  `CreateStreamProcessor`. You might not be able to use the same name for a stream
  processor for a few seconds after calling `DeleteStreamProcessor`.
  """
  @spec delete_stream_processor(map(), delete_stream_processor_request(), list()) ::
          {:ok, delete_stream_processor_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, delete_stream_processor_errors()}
  def delete_stream_processor(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DeleteStreamProcessor", input, options)
  end

  @doc """
  Deletes the specified UserID within the collection.

  Faces that are associated with the
  UserID are disassociated from the UserID before deleting the specified UserID.
  If the
  specified `Collection` or `UserID` is already deleted or not found, a
  `ResourceNotFoundException` will be thrown. If the action is successful with a
  200 response, an empty HTTP body is returned.
  """
  @spec delete_user(map(), delete_user_request(), list()) ::
          {:ok, delete_user_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, delete_user_errors()}
  def delete_user(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DeleteUser", input, options)
  end

  @doc """
  Describes the specified collection.

  You can use `DescribeCollection` to get
  information, such as the number of faces indexed into a collection and the
  version of the
  model used by the collection for face detection.

  For more information, see Describing a Collection in the
  Amazon Rekognition Developer Guide.
  """
  @spec describe_collection(map(), describe_collection_request(), list()) ::
          {:ok, describe_collection_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, describe_collection_errors()}
  def describe_collection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DescribeCollection", input, options)
  end

  @doc """

  This operation applies only to Amazon Rekognition Custom Labels.

  Describes an Amazon Rekognition Custom Labels dataset. You can get information
  such as the current status of a dataset and
  statistics about the images and labels in a dataset.

  This operation requires permissions to perform the `rekognition:DescribeDataset`
  action.
  """
  @spec describe_dataset(map(), describe_dataset_request(), list()) ::
          {:ok, describe_dataset_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, describe_dataset_errors()}
  def describe_dataset(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DescribeDataset", input, options)
  end

  @doc """
  Lists and describes the versions of an Amazon Rekognition project.

  You can specify up to 10 model or
  adapter versions in `ProjectVersionArns`. If you don't specify a value,
  descriptions for all model/adapter versions in the project are returned.

  This operation requires permissions to perform the
  `rekognition:DescribeProjectVersions`
  action.
  """
  @spec describe_project_versions(map(), describe_project_versions_request(), list()) ::
          {:ok, describe_project_versions_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, describe_project_versions_errors()}
  def describe_project_versions(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DescribeProjectVersions", input, options)
  end

  @doc """
  Gets information about your Rekognition projects.

  This operation requires permissions to perform the
  `rekognition:DescribeProjects` action.
  """
  @spec describe_projects(map(), describe_projects_request(), list()) ::
          {:ok, describe_projects_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, describe_projects_errors()}
  def describe_projects(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DescribeProjects", input, options)
  end

  @doc """
  Provides information about a stream processor created by
  `CreateStreamProcessor`.

  You can get information about the input and output streams, the input parameters
  for the face recognition being performed,
  and the current status of the stream processor.
  """
  @spec describe_stream_processor(map(), describe_stream_processor_request(), list()) ::
          {:ok, describe_stream_processor_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, describe_stream_processor_errors()}
  def describe_stream_processor(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DescribeStreamProcessor", input, options)
  end

  @doc """

  This operation applies only to Amazon Rekognition Custom Labels.

  Detects custom labels in a supplied image by using an Amazon Rekognition Custom
  Labels model.

  You specify which version of a model version to use by using the
  `ProjectVersionArn` input
  parameter.

  You pass the input image as base64-encoded image bytes or as a reference to an
  image in
  an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition
  operations, passing
  image bytes is not supported. The image must be either a PNG or JPEG formatted
  file.

  For each object that the model version detects on an image, the API returns a
  (`CustomLabel`) object in an array (`CustomLabels`).
  Each `CustomLabel` object provides the label name (`Name`), the level
  of confidence that the image contains the object (`Confidence`), and
  object location information, if it exists, for the label on the image
  (`Geometry`).

  To filter labels that are returned, specify a value for `MinConfidence`.
  `DetectCustomLabelsLabels` only returns labels with a confidence that's higher
  than
  the specified value.

  The value of `MinConfidence` maps to the assumed threshold values
  created during training. For more information, see *Assumed threshold*
  in the Amazon Rekognition Custom Labels Developer Guide.
  Amazon Rekognition Custom Labels metrics expresses an assumed threshold as a
  floating point value between 0-1. The range of
  `MinConfidence` normalizes the threshold value to a percentage value (0-100).
  Confidence
  responses from `DetectCustomLabels` are also returned as a percentage.
  You can use `MinConfidence` to change the precision and recall or your model.
  For more information, see
  *Analyzing an image* in the Amazon Rekognition Custom Labels Developer Guide.

  If you don't specify a value for `MinConfidence`, `DetectCustomLabels`
  returns labels based on the assumed threshold of each label.

  This is a stateless API operation. That is, the operation does not persist any
  data.

  This operation requires permissions to perform the
  `rekognition:DetectCustomLabels` action.

  For more information, see
  *Analyzing an image* in the Amazon Rekognition Custom Labels Developer Guide.
  """
  @spec detect_custom_labels(map(), detect_custom_labels_request(), list()) ::
          {:ok, detect_custom_labels_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, detect_custom_labels_errors()}
  def detect_custom_labels(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DetectCustomLabels", input, options)
  end

  @doc """
  Detects faces within an image that is provided as input.

  `DetectFaces` detects the 100 largest faces in the image. For each face
  detected, the operation returns face details. These details include a bounding
  box of the
  face, a confidence value (that the bounding box contains a face), and a fixed
  set of
  attributes such as facial landmarks (for example, coordinates of eye and mouth),
  pose,
  presence of facial occlusion, and so on.

  The face-detection algorithm is most effective on frontal faces. For non-frontal
  or
  obscured faces, the algorithm might not detect the faces or might detect faces
  with lower
  confidence.

  You pass the input image either as base64-encoded image bytes or as a reference
  to an
  image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition
  operations,
  passing image bytes is not supported. The image must be either a PNG or JPEG
  formatted file.

  This is a stateless API operation. That is, the operation does not persist any
  data.

  This operation requires permissions to perform the `rekognition:DetectFaces`
  action.
  """
  @spec detect_faces(map(), detect_faces_request(), list()) ::
          {:ok, detect_faces_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, detect_faces_errors()}
  def detect_faces(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DetectFaces", input, options)
  end

  @doc """
  Detects instances of real-world entities within an image (JPEG or PNG) provided
  as
  input.

  This includes objects like flower, tree, and table; events like wedding,
  graduation,
  and birthday party; and concepts like landscape, evening, and nature.

  For an example, see Analyzing images stored in an Amazon S3 bucket in the
  Amazon Rekognition Developer Guide.

  You pass the input image as base64-encoded image bytes or as a reference to an
  image in
  an Amazon S3 bucket. If you use the
  AWS
  CLI to call Amazon Rekognition operations, passing image bytes is not
  supported. The image must be either a PNG or JPEG formatted file.

  ## Optional Parameters

  You can specify one or both of the `GENERAL_LABELS` and
  `IMAGE_PROPERTIES` feature types when calling the DetectLabels API. Including
  `GENERAL_LABELS` will ensure the response includes the labels detected in the
  input image, while including `IMAGE_PROPERTIES `will ensure the response
  includes
  information about the image quality and color.

  When using `GENERAL_LABELS` and/or `IMAGE_PROPERTIES` you can
  provide filtering criteria to the Settings parameter. You can filter with sets
  of individual
  labels or with label categories. You can specify inclusive filters, exclusive
  filters, or a
  combination of inclusive and exclusive filters. For more information on
  filtering see [Detecting Labels in an
  Image](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html).

  When getting labels, you can specify `MinConfidence` to control the
  confidence threshold for the labels returned. The default is 55%. You can also
  add the
  `MaxLabels` parameter to limit the number of labels returned. The default and
  upper limit is 1000 labels. These arguments are only valid when supplying
  GENERAL_LABELS as a
  feature type.

  ## Response Elements

  For each object, scene, and concept the API returns one or more labels. The API
  returns the following types of information about labels:

    *
  Name - The name of the detected label.

    *
  Confidence - The level of confidence in the label assigned to a detected object.

    *
  Parents - The ancestor labels for a detected label. DetectLabels returns a
  hierarchical taxonomy of detected labels. For example, a detected car might be
  assigned
  the label car. The label car has two parent labels: Vehicle (its parent) and
  Transportation (its grandparent). The response includes the all ancestors for a
  label,
  where every ancestor is a unique label. In the previous example, Car, Vehicle,
  and
  Transportation are returned as unique labels in the response.

    *
  Aliases - Possible Aliases for the label.

    *
  Categories - The label categories that the detected label belongs to.

    *
  BoundingBox — Bounding boxes are described for all instances of detected common
  object labels, returned in an array of Instance objects. An Instance object
  contains a
  BoundingBox object, describing the location of the label on the input image. It
  also
  includes the confidence for the accuracy of the detected bounding box.

  The API returns the following information regarding the image, as part of the
  ImageProperties structure:

    *
  Quality - Information about the Sharpness, Brightness, and Contrast of the input
  image, scored between 0 to 100. Image quality is returned for the entire image,
  as well as
  the background and the foreground.

    *
  Dominant Color - An array of the dominant colors in the image.

    *
  Foreground - Information about the sharpness, brightness, and dominant colors of
  the
  input image’s foreground.

    *
  Background - Information about the sharpness, brightness, and dominant colors of
  the
  input image’s background.

  The list of returned labels will include at least one label for every detected
  object,
  along with information about that label. In the following example, suppose the
  input image has
  a lighthouse, the sea, and a rock. The response includes all three labels, one
  for each
  object, as well as the confidence in the label:

  `{Name: lighthouse, Confidence: 98.4629}`

  `{Name: rock,Confidence: 79.2097}`

  ` {Name: sea,Confidence: 75.061}`

  The list of labels can include multiple labels for the same object. For example,
  if the
  input image shows a flower (for example, a tulip), the operation might return
  the following
  three labels.

  `{Name: flower,Confidence: 99.0562}`

  `{Name: plant,Confidence: 99.0562}`

  `{Name: tulip,Confidence: 99.0562}`

  In this example, the detection algorithm more precisely identifies the flower as
  a
  tulip.

  If the object detected is a person, the operation doesn't provide the same
  facial
  details that the `DetectFaces` operation provides.

  This is a stateless API operation that doesn't return any data.

  This operation requires permissions to perform the
  `rekognition:DetectLabels` action.
  """
  @spec detect_labels(map(), detect_labels_request(), list()) ::
          {:ok, detect_labels_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, detect_labels_errors()}
  def detect_labels(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DetectLabels", input, options)
  end

  @doc """
  Detects unsafe content in a specified JPEG or PNG format image.

  Use
  `DetectModerationLabels` to moderate images depending on your requirements. For
  example, you might want to filter images that contain nudity, but not images
  containing
  suggestive content.

  To filter images, use the labels returned by `DetectModerationLabels` to
  determine which types of content are appropriate.

  For information about moderation labels, see Detecting Unsafe Content in the
  Amazon Rekognition Developer Guide.

  You pass the input image either as base64-encoded image bytes or as a reference
  to an
  image in an Amazon S3 bucket. If you use the
  AWS
  CLI to call Amazon Rekognition operations, passing image bytes is not
  supported. The image must be either a PNG or JPEG formatted file.

  You can specify an adapter to use when retrieving label predictions by providing
  a
  `ProjectVersionArn` to the `ProjectVersion` argument.
  """
  @spec detect_moderation_labels(map(), detect_moderation_labels_request(), list()) ::
          {:ok, detect_moderation_labels_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, detect_moderation_labels_errors()}
  def detect_moderation_labels(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DetectModerationLabels", input, options)
  end

  @doc """
  Detects Personal Protective Equipment (PPE) worn by people detected in an image.

  Amazon Rekognition can detect the
  following types of PPE.

    *
  Face cover

    *
  Hand cover

    *
  Head cover

  You pass the input image as base64-encoded image bytes or as a reference to an
  image in an Amazon S3 bucket.
  The image must be either a PNG or JPG formatted file.

  `DetectProtectiveEquipment` detects PPE worn by up to 15 persons detected in an
  image.

  For each person detected in the image the API returns an array of body parts
  (face, head, left-hand, right-hand).
  For each body part, an array of detected items of PPE is returned, including an
  indicator of whether or not the PPE
  covers the body part. The API returns the confidence it has in each detection
  (person, PPE, body part and body part coverage). It also returns a bounding box
  (`BoundingBox`) for each detected
  person and each detected item of PPE.

  You can optionally request a summary of detected PPE items with the
  `SummarizationAttributes` input parameter.
  The summary provides the following information.

    *
  The persons detected as wearing all of the types of PPE that you specify.

    *
  The persons detected as not wearing all of the types PPE that you specify.

    *
  The persons detected where PPE adornment could not be determined.

  This is a stateless API operation. That is, the operation does not persist any
  data.

  This operation requires permissions to perform the
  `rekognition:DetectProtectiveEquipment` action.
  """
  @spec detect_protective_equipment(map(), detect_protective_equipment_request(), list()) ::
          {:ok, detect_protective_equipment_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, detect_protective_equipment_errors()}
  def detect_protective_equipment(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DetectProtectiveEquipment", input, options)
  end

  @doc """
  Detects text in the input image and converts it into machine-readable text.

  Pass the input image as base64-encoded image bytes or as a reference to an image
  in an
  Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition operations,
  you must pass it as a
  reference to an image in an Amazon S3 bucket. For the AWS CLI, passing image
  bytes is not
  supported. The image must be either a .png or .jpeg formatted file.

  The `DetectText` operation returns text in an array of `TextDetection` elements,
  `TextDetections`. Each
  `TextDetection` element provides information about a single word or line of text
  that was detected in the image.

  A word is one or more script characters that are not separated by spaces.
  `DetectText` can detect up to 100 words in an image.

  A line is a string of equally spaced words. A line isn't necessarily a complete
  sentence. For example, a driver's license number is detected as a line. A line
  ends when there
  is no aligned text after it. Also, a line ends when there is a large gap between
  words,
  relative to the length of the words. This means, depending on the gap between
  words, Amazon Rekognition
  may detect multiple lines in text aligned in the same direction. Periods don't
  represent the
  end of a line. If a sentence spans multiple lines, the `DetectText` operation
  returns multiple lines.

  To determine whether a `TextDetection` element is a line of text or a word,
  use the `TextDetection` object `Type` field.

  To be detected, text must be within +/- 90 degrees orientation of the horizontal
  axis.

  For more information, see Detecting text in the Amazon Rekognition Developer
  Guide.
  """
  @spec detect_text(map(), detect_text_request(), list()) ::
          {:ok, detect_text_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, detect_text_errors()}
  def detect_text(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DetectText", input, options)
  end

  @doc """
  Removes the association between a `Face` supplied in an array of
  `FaceIds` and the User.

  If the User is not present already, then a
  `ResourceNotFound` exception is thrown. If successful, an array of faces that
  are
  disassociated from the User is returned. If a given face is already
  disassociated from the
  given UserID, it will be ignored and not be returned in the response. If a given
  face is
  already associated with a different User or not found in the collection it will
  be returned as
  part of `UnsuccessfulDisassociations`. You can remove 1 - 100 face IDs from a
  user
  at one time.
  """
  @spec disassociate_faces(map(), disassociate_faces_request(), list()) ::
          {:ok, disassociate_faces_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, disassociate_faces_errors()}
  def disassociate_faces(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DisassociateFaces", input, options)
  end

  @doc """

  This operation applies only to Amazon Rekognition Custom Labels.

  Distributes the entries (images) in a training dataset across the training
  dataset and the test dataset for a project.
  `DistributeDatasetEntries` moves 20% of the training dataset images to the test
  dataset.
  An entry is a JSON Line that describes an image.

  You supply the Amazon Resource Names (ARN) of a project's training dataset and
  test dataset.
  The training dataset must contain the images that you want to split. The test
  dataset
  must be empty. The datasets must belong to the same project. To create training
  and test datasets for a project, call `CreateDataset`.

  Distributing a dataset takes a while to complete. To check the status call
  `DescribeDataset`. The operation
  is complete when the `Status` field for the training dataset and the test
  dataset is `UPDATE_COMPLETE`.
  If the dataset split fails, the value of `Status` is `UPDATE_FAILED`.

  This operation requires permissions to perform the
  `rekognition:DistributeDatasetEntries` action.
  """
  @spec distribute_dataset_entries(map(), distribute_dataset_entries_request(), list()) ::
          {:ok, distribute_dataset_entries_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, distribute_dataset_entries_errors()}
  def distribute_dataset_entries(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "DistributeDatasetEntries", input, options)
  end

  @doc """
  Gets the name and additional information about a celebrity based on their Amazon
  Rekognition ID.

  The additional information is returned as an array of URLs. If there is no
  additional
  information about the celebrity, this list is empty.

  For more information, see Getting information about a celebrity in the
  Amazon Rekognition Developer Guide.

  This operation requires permissions to perform the
  `rekognition:GetCelebrityInfo` action.
  """
  @spec get_celebrity_info(map(), get_celebrity_info_request(), list()) ::
          {:ok, get_celebrity_info_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, get_celebrity_info_errors()}
  def get_celebrity_info(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetCelebrityInfo", input, options)
  end

  @doc """
  Gets the celebrity recognition results for a Amazon Rekognition Video analysis
  started by
  `StartCelebrityRecognition`.

  Celebrity recognition in a video is an asynchronous operation. Analysis is
  started by a
  call to `StartCelebrityRecognition` which returns a job identifier
  (`JobId`).

  When the celebrity recognition operation finishes, Amazon Rekognition Video
  publishes a completion
  status to the Amazon Simple Notification Service topic registered in the initial
  call to
  `StartCelebrityRecognition`. To get the results of the celebrity recognition
  analysis, first check that the status value published to the Amazon SNS topic is
  `SUCCEEDED`. If so, call `GetCelebrityDetection` and pass the job
  identifier (`JobId`) from the initial call to `StartCelebrityDetection`.

  For more information, see Working With Stored Videos in the Amazon Rekognition
  Developer Guide.

  `GetCelebrityRecognition` returns detected celebrities and the time(s) they
  are detected in an array (`Celebrities`) of `CelebrityRecognition`
  objects. Each `CelebrityRecognition`
  contains information about the celebrity in a `CelebrityDetail` object and the
  time, `Timestamp`, the celebrity was detected. This `CelebrityDetail` object
  stores information about the detected celebrity's face
  attributes, a face bounding box, known gender, the celebrity's name, and a
  confidence
  estimate.

  `GetCelebrityRecognition` only returns the default facial
  attributes (`BoundingBox`, `Confidence`, `Landmarks`,
  `Pose`, and `Quality`). The `BoundingBox` field only
  applies to the detected face instance. The other facial attributes listed in the
  `Face` object of the following response syntax are not returned. For more
  information, see FaceDetail in the Amazon Rekognition Developer Guide.

  By default, the `Celebrities` array is sorted by time (milliseconds from the
  start of the video).
  You can also sort the array by celebrity by specifying the value `ID` in the
  `SortBy` input parameter.

  The `CelebrityDetail` object includes the celebrity identifer and additional
  information urls. If you don't store
  the additional information urls, you can get them later by calling
  `GetCelebrityInfo` with the celebrity identifer.

  No information is returned for faces not recognized as celebrities.

  Use MaxResults parameter to limit the number of labels returned. If there are
  more results than
  specified in `MaxResults`, the value of `NextToken` in the operation response
  contains a
  pagination token for getting the next set of results. To get the next page of
  results, call `GetCelebrityDetection`
  and populate the `NextToken` request parameter with the token
  value returned from the previous call to `GetCelebrityRecognition`.
  """
  @spec get_celebrity_recognition(map(), get_celebrity_recognition_request(), list()) ::
          {:ok, get_celebrity_recognition_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, get_celebrity_recognition_errors()}
  def get_celebrity_recognition(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetCelebrityRecognition", input, options)
  end

  @doc """
  Gets the inappropriate, unwanted, or offensive content analysis results for a
  Amazon Rekognition Video analysis started by
  `StartContentModeration`.

  For a list of moderation labels in Amazon Rekognition, see
  [Using the image and video moderation APIs](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api).

  Amazon Rekognition Video inappropriate or offensive content detection in a
  stored video is an asynchronous operation. You start analysis by calling
  `StartContentModeration` which returns a job identifier (`JobId`).
  When analysis finishes, Amazon Rekognition Video publishes a completion status
  to the Amazon Simple Notification Service
  topic registered in the initial call to `StartContentModeration`.
  To get the results of the content analysis, first check that the status value
  published to the Amazon SNS
  topic is `SUCCEEDED`. If so, call `GetContentModeration` and pass the job
  identifier
  (`JobId`) from the initial call to `StartContentModeration`.

  For more information, see Working with Stored Videos in the
  Amazon Rekognition Devlopers Guide.

  `GetContentModeration` returns detected inappropriate, unwanted, or offensive
  content moderation labels,
  and the time they are detected, in an array, `ModerationLabels`, of
  `ContentModerationDetection` objects.

  By default, the moderated labels are returned sorted by time, in milliseconds
  from the start of the
  video. You can also sort them by moderated label by specifying `NAME` for the
  `SortBy`
  input parameter.

  Since video analysis can return a large number of results, use the `MaxResults`
  parameter to limit
  the number of labels returned in a single call to `GetContentModeration`. If
  there are more results than
  specified in `MaxResults`, the value of `NextToken` in the operation response
  contains a
  pagination token for getting the next set of results. To get the next page of
  results, call `GetContentModeration`
  and populate the `NextToken` request parameter with the value of `NextToken`
  returned from the previous call to `GetContentModeration`.

  For more information, see moderating content in the Amazon Rekognition Developer
  Guide.
  """
  @spec get_content_moderation(map(), get_content_moderation_request(), list()) ::
          {:ok, get_content_moderation_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, get_content_moderation_errors()}
  def get_content_moderation(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetContentModeration", input, options)
  end

  @doc """
  Gets face detection results for a Amazon Rekognition Video analysis started by
  `StartFaceDetection`.

  Face detection with Amazon Rekognition Video is an asynchronous operation. You
  start face detection by calling `StartFaceDetection`
  which returns a job identifier (`JobId`). When the face detection operation
  finishes, Amazon Rekognition Video publishes a completion status to
  the Amazon Simple Notification Service topic registered in the initial call to
  `StartFaceDetection`. To get the results
  of the face detection operation, first check that the status value published to
  the Amazon SNS topic is `SUCCEEDED`.
  If so, call `GetFaceDetection` and pass the job identifier
  (`JobId`) from the initial call to `StartFaceDetection`.

  `GetFaceDetection` returns an array of detected faces (`Faces`) sorted by the
  time the faces were detected.

  Use MaxResults parameter to limit the number of labels returned. If there are
  more results than
  specified in `MaxResults`, the value of `NextToken` in the operation response
  contains a pagination token for getting the next set
  of results. To get the next page of results, call `GetFaceDetection` and
  populate the `NextToken` request parameter with the token
  value returned from the previous call to `GetFaceDetection`.

  Note that for the `GetFaceDetection` operation, the returned values for
  `FaceOccluded` and `EyeDirection` will always be "null".
  """
  @spec get_face_detection(map(), get_face_detection_request(), list()) ::
          {:ok, get_face_detection_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, get_face_detection_errors()}
  def get_face_detection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetFaceDetection", input, options)
  end

  @doc """
  Retrieves the results of a specific Face Liveness session.

  It requires the
  `sessionId` as input, which was created using
  `CreateFaceLivenessSession`. Returns the corresponding Face Liveness confidence
  score, a reference image that includes a face bounding box, and audit images
  that also contain
  face bounding boxes. The Face Liveness confidence score ranges from 0 to 100.

  The number of audit images returned by `GetFaceLivenessSessionResults` is
  defined by the `AuditImagesLimit` paramater when calling
  `CreateFaceLivenessSession`. Reference images are always returned when
  possible.
  """
  @spec get_face_liveness_session_results(
          map(),
          get_face_liveness_session_results_request(),
          list()
        ) ::
          {:ok, get_face_liveness_session_results_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, get_face_liveness_session_results_errors()}
  def get_face_liveness_session_results(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetFaceLivenessSessionResults", input, options)
  end

  @doc """
  Gets the face search results for Amazon Rekognition Video face search started by
  `StartFaceSearch`.

  The search returns faces in a collection that match the faces
  of persons detected in a video. It also includes the time(s) that faces are
  matched in the video.

  Face search in a video is an asynchronous operation. You start face search by
  calling
  to `StartFaceSearch` which returns a job identifier (`JobId`).
  When the search operation finishes, Amazon Rekognition Video publishes a
  completion status to the Amazon Simple Notification Service
  topic registered in the initial call to `StartFaceSearch`.
  To get the search results, first check that the status value published to the
  Amazon SNS
  topic is `SUCCEEDED`. If so, call `GetFaceSearch` and pass the job identifier
  (`JobId`) from the initial call to `StartFaceSearch`.

  For more information, see Searching Faces in a Collection in the
  Amazon Rekognition Developer Guide.

  The search results are retured in an array, `Persons`, of
  `PersonMatch` objects. Each`PersonMatch` element contains
  details about the matching faces in the input collection, person information
  (facial attributes,
  bounding boxes, and person identifer)
  for the matched person, and the time the person was matched in the video.

  `GetFaceSearch` only returns the default
  facial attributes (`BoundingBox`, `Confidence`,
  `Landmarks`, `Pose`, and `Quality`). The other facial attributes listed
  in the `Face` object of the following response syntax are not returned. For more
  information,
  see FaceDetail in the Amazon Rekognition Developer Guide.

  By default, the `Persons` array is sorted by the time, in milliseconds from the
  start of the video, persons are matched.
  You can also sort by persons by specifying `INDEX` for the `SORTBY` input
  parameter.
  """
  @spec get_face_search(map(), get_face_search_request(), list()) ::
          {:ok, get_face_search_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, get_face_search_errors()}
  def get_face_search(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetFaceSearch", input, options)
  end

  @doc """
  Gets the label detection results of a Amazon Rekognition Video analysis started
  by `StartLabelDetection`.

  The label detection operation is started by a call to `StartLabelDetection`
  which returns a job identifier (`JobId`). When
  the label detection operation finishes, Amazon Rekognition publishes a
  completion status to the
  Amazon Simple Notification Service topic registered in the initial call to
  `StartlabelDetection`.

  To get the results of the label detection operation, first check that the status
  value
  published to the Amazon SNS topic is `SUCCEEDED`. If so, call
  `GetLabelDetection` and pass the job identifier (`JobId`) from the
  initial call to `StartLabelDetection`.

  `GetLabelDetection` returns an array of detected labels
  (`Labels`) sorted by the time the labels were detected. You can also sort by the
  label name by specifying `NAME` for the `SortBy` input parameter. If
  there is no `NAME` specified, the default sort is by
  timestamp.

  You can select how results are aggregated by using the `AggregateBy` input
  parameter. The default aggregation method is `TIMESTAMPS`. You can also
  aggregate
  by `SEGMENTS`, which aggregates all instances of labels detected in a given
  segment.

  The returned Labels array may include the following attributes:

    *
  Name - The name of the detected label.

    *
  Confidence - The level of confidence in the label assigned to a detected object.

    *
  Parents - The ancestor labels for a detected label. GetLabelDetection returns a
  hierarchical
  taxonomy of detected labels. For example, a detected car might be assigned the
  label car.
  The label car has two parent labels: Vehicle (its parent) and Transportation
  (its
  grandparent). The response includes the all ancestors for a label, where every
  ancestor is
  a unique label. In the previous example, Car, Vehicle, and Transportation are
  returned as
  unique labels in the response.

    *
  Aliases - Possible Aliases for the label.

    *
  Categories - The label categories that the detected label belongs to.

    *
  BoundingBox — Bounding boxes are described for all instances of detected common
  object labels,
  returned in an array of Instance objects. An Instance object contains a
  BoundingBox object, describing
  the location of the label on the input image. It also includes the confidence
  for the accuracy of the detected bounding box.

    *
  Timestamp - Time, in milliseconds from the start of the video, that the label
  was detected.
  For aggregation by `SEGMENTS`, the `StartTimestampMillis`,
  `EndTimestampMillis`, and `DurationMillis` structures are what
  define a segment. Although the “Timestamp” structure is still returned with each
  label,
  its value is set to be the same as `StartTimestampMillis`.

  Timestamp and Bounding box information are returned for detected Instances, only
  if
  aggregation is done by `TIMESTAMPS`. If aggregating by `SEGMENTS`,
  information about detected instances isn’t returned.

  The version of the label model used for the detection is also returned.

  ## Note `DominantColors` isn't returned for `Instances`,
  although it is shown as part of the response in the sample seen below.

  Use `MaxResults` parameter to limit the number of labels returned. If
  there are more results than specified in `MaxResults`, the value of
  `NextToken` in the operation response contains a pagination token for getting
  the
  next set of results. To get the next page of results, call `GetlabelDetection`
  and
  populate the `NextToken` request parameter with the token value returned from
  the
  previous call to `GetLabelDetection`.

  If you are retrieving results while using the Amazon Simple Notification
  Service, note that you will receive an
  "ERROR" notification if the job encounters an issue.
  """
  @spec get_label_detection(map(), get_label_detection_request(), list()) ::
          {:ok, get_label_detection_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, get_label_detection_errors()}
  def get_label_detection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetLabelDetection", input, options)
  end

  @doc """
  Retrieves the results for a given media analysis job.

  Takes a `JobId` returned by StartMediaAnalysisJob.
  """
  @spec get_media_analysis_job(map(), get_media_analysis_job_request(), list()) ::
          {:ok, get_media_analysis_job_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, get_media_analysis_job_errors()}
  def get_media_analysis_job(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetMediaAnalysisJob", input, options)
  end

  @doc """


  *End of support notice:* On October 31, 2025, AWS will discontinue
  support for Amazon Rekognition People Pathing.

  After October 31, 2025, you will no
  longer be able to use the Rekognition People Pathing capability. For more
  information,
  visit this [blog post](https://aws.amazon.com/blogs/machine-learning/transitioning-from-amazon-rekognition-people-pathing-exploring-other-alternatives/).

  Gets the path tracking results of a Amazon Rekognition Video analysis started by
  `StartPersonTracking`.

  The person path tracking operation is started by a call to `StartPersonTracking`
  which returns a job identifier (`JobId`). When the operation finishes, Amazon
  Rekognition Video publishes a completion status to
  the Amazon Simple Notification Service topic registered in the initial call to
  `StartPersonTracking`.

  To get the results of the person path tracking operation, first check
  that the status value published to the Amazon SNS topic is `SUCCEEDED`.
  If so, call `GetPersonTracking` and pass the job identifier
  (`JobId`) from the initial call to `StartPersonTracking`.

  `GetPersonTracking` returns an array, `Persons`, of tracked persons and the
  time(s) their
  paths were tracked in the video.

  `GetPersonTracking` only returns the default
  facial attributes (`BoundingBox`, `Confidence`,
  `Landmarks`, `Pose`, and `Quality`). The other facial attributes listed
  in the `Face` object of the following response syntax are not returned.

  For more information, see FaceDetail in the Amazon Rekognition Developer Guide.

  By default, the array is sorted by the time(s) a person's path is tracked in the
  video.
  You can sort by tracked persons by specifying `INDEX` for the `SortBy` input
  parameter.

  Use the `MaxResults` parameter to limit the number of items returned. If there
  are more results than
  specified in `MaxResults`, the value of `NextToken` in the operation response
  contains a pagination token for getting the next set
  of results. To get the next page of results, call `GetPersonTracking` and
  populate the `NextToken` request parameter with the token
  value returned from the previous call to `GetPersonTracking`.
  """
  @spec get_person_tracking(map(), get_person_tracking_request(), list()) ::
          {:ok, get_person_tracking_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, get_person_tracking_errors()}
  def get_person_tracking(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetPersonTracking", input, options)
  end

  @doc """
  Gets the segment detection results of a Amazon Rekognition Video analysis
  started by `StartSegmentDetection`.

  Segment detection with Amazon Rekognition Video is an asynchronous operation.
  You start segment detection by
  calling `StartSegmentDetection` which returns a job identifier (`JobId`).
  When the segment detection operation finishes, Amazon Rekognition publishes a
  completion status to the Amazon Simple Notification Service
  topic registered in the initial call to `StartSegmentDetection`. To get the
  results
  of the segment detection operation, first check that the status value published
  to the Amazon SNS topic is `SUCCEEDED`.
  if so, call `GetSegmentDetection` and pass the job identifier (`JobId`) from the
  initial call
  of `StartSegmentDetection`.

  `GetSegmentDetection` returns detected segments in an array (`Segments`)
  of `SegmentDetection` objects. `Segments` is sorted by the segment types
  specified in the `SegmentTypes` input parameter of `StartSegmentDetection`.
  Each element of the array includes the detected segment, the precentage
  confidence in the acuracy
  of the detected segment, the type of the segment, and the frame in which the
  segment was detected.

  Use `SelectedSegmentTypes` to find out the type of segment detection requested
  in the
  call to `StartSegmentDetection`.

  Use the `MaxResults` parameter to limit the number of segment detections
  returned. If there are more results than
  specified in `MaxResults`, the value of `NextToken` in the operation response
  contains
  a pagination token for getting the next set of results. To get the next page of
  results, call `GetSegmentDetection`
  and populate the `NextToken` request parameter with the token value returned
  from the previous
  call to `GetSegmentDetection`.

  For more information, see Detecting video segments in stored video in the Amazon
  Rekognition Developer Guide.
  """
  @spec get_segment_detection(map(), get_segment_detection_request(), list()) ::
          {:ok, get_segment_detection_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, get_segment_detection_errors()}
  def get_segment_detection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetSegmentDetection", input, options)
  end

  @doc """
  Gets the text detection results of a Amazon Rekognition Video analysis started
  by `StartTextDetection`.

  Text detection with Amazon Rekognition Video is an asynchronous operation. You
  start text detection by
  calling `StartTextDetection` which returns a job identifier (`JobId`)
  When the text detection operation finishes, Amazon Rekognition publishes a
  completion status to the Amazon Simple Notification Service
  topic registered in the initial call to `StartTextDetection`. To get the results
  of the text detection operation, first check that the status value published to
  the Amazon SNS topic is `SUCCEEDED`.
  if so, call `GetTextDetection` and pass the job identifier (`JobId`) from the
  initial call
  of `StartLabelDetection`.

  `GetTextDetection` returns an array of detected text (`TextDetections`) sorted
  by
  the time the text was detected, up to 100 words per frame of video.

  Each element of the array includes the detected text, the precentage confidence
  in the acuracy
  of the detected text, the time the text was detected, bounding box information
  for where the text
  was located, and unique identifiers for words and their lines.

  Use MaxResults parameter to limit the number of text detections returned. If
  there are more results than
  specified in `MaxResults`, the value of `NextToken` in the operation response
  contains
  a pagination token for getting the next set of results. To get the next page of
  results, call `GetTextDetection`
  and populate the `NextToken` request parameter with the token value returned
  from the previous
  call to `GetTextDetection`.
  """
  @spec get_text_detection(map(), get_text_detection_request(), list()) ::
          {:ok, get_text_detection_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, get_text_detection_errors()}
  def get_text_detection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "GetTextDetection", input, options)
  end

  @doc """
  Detects faces in the input image and adds them to the specified collection.

  Amazon Rekognition doesn't save the actual faces that are detected. Instead, the
  underlying
  detection algorithm first detects the faces in the input image. For each face,
  the algorithm
  extracts facial features into a feature vector, and stores it in the backend
  database.
  Amazon Rekognition uses feature vectors when it performs face match and search
  operations using the
  `SearchFaces` and `SearchFacesByImage` operations.

  For more information, see Adding faces to a collection in the Amazon Rekognition
  Developer Guide.

  To get the number of faces in a collection, call `DescribeCollection`.

  If you're using version 1.0 of the face detection model, `IndexFaces`
  indexes the 15 largest faces in the input image. Later versions of the face
  detection model
  index the 100 largest faces in the input image.

  If you're using version 4 or later of the face model, image orientation
  information is not
  returned in the `OrientationCorrection` field.

  To determine which version of the model you're using, call `DescribeCollection`
  and supply the collection ID. You can also get the model
  version from the value of `FaceModelVersion` in the response from
  `IndexFaces`

  For more information, see Model Versioning in the Amazon Rekognition Developer
  Guide.

  If you provide the optional `ExternalImageId` for the input image you
  provided, Amazon Rekognition associates this ID with all faces that it detects.
  When you call the `ListFaces` operation, the response returns the external ID.
  You can use this
  external image ID to create a client-side index to associate the faces with each
  image. You
  can then use the index to find all faces in an image.

  You can specify the maximum number of faces to index with the `MaxFaces` input
  parameter. This is useful when you want to index the largest faces in an image
  and don't want
  to index smaller faces, such as those belonging to people standing in the
  background.

  The `QualityFilter` input parameter allows you to filter out detected faces
  that don’t meet a required quality bar. The quality bar is based on a variety of
  common use
  cases. By default, `IndexFaces` chooses the quality bar that's used to filter
  faces. You can also explicitly choose the quality bar. Use `QualityFilter`, to
  set
  the quality bar by specifying `LOW`, `MEDIUM`, or `HIGH`. If
  you do not want to filter detected faces, specify `NONE`.

  To use quality filtering, you need a collection associated with version 3 of the
  face
  model or higher. To get the version of the face model associated with a
  collection, call
  `DescribeCollection`.

  Information about faces detected in an image, but not indexed, is returned in an
  array of
  `UnindexedFace` objects, `UnindexedFaces`. Faces aren't indexed
  for reasons such as:

    *
  The number of faces detected exceeds the value of the `MaxFaces` request
  parameter.

    *
  The face is too small compared to the image dimensions.

    *
  The face is too blurry.

    *
  The image is too dark.

    *
  The face has an extreme pose.

    *
  The face doesn’t have enough detail to be suitable for face search.

  In response, the `IndexFaces` operation returns an array of metadata for all
  detected faces, `FaceRecords`. This includes:

    *
  The bounding box, `BoundingBox`, of the detected face.

    *
  A confidence value, `Confidence`, which indicates the confidence that the
  bounding box contains a face.

    *
  A face ID, `FaceId`, assigned by the service for each face that's detected
  and stored.

    *
  An image ID, `ImageId`, assigned by the service for the input image.

  If you request `ALL` or specific facial attributes (e.g.,
  `FACE_OCCLUDED`) by using the detectionAttributes parameter, Amazon Rekognition
  returns detailed facial attributes, such as facial landmarks (for example,
  location of eye and
  mouth), facial occlusion, and other facial attributes.

  If you provide the same image, specify the same collection, and use the same
  external ID
  in the `IndexFaces` operation, Amazon Rekognition doesn't save duplicate face
  metadata.

  The input image is passed either as base64-encoded image bytes, or as a
  reference to an
  image in an Amazon S3 bucket. If you use the AWS CLI to call Amazon Rekognition
  operations,
  passing image bytes isn't supported. The image must be formatted as a PNG or
  JPEG file.

  This operation requires permissions to perform the `rekognition:IndexFaces`
  action.
  """
  @spec index_faces(map(), index_faces_request(), list()) ::
          {:ok, index_faces_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, index_faces_errors()}
  def index_faces(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "IndexFaces", input, options)
  end

  @doc """
  Returns list of collection IDs in your account.

  If the result is truncated, the
  response also provides a `NextToken` that you can use in the subsequent request
  to
  fetch the next set of collection IDs.

  For an example, see Listing collections in the Amazon Rekognition Developer
  Guide.

  This operation requires permissions to perform the
  `rekognition:ListCollections` action.
  """
  @spec list_collections(map(), list_collections_request(), list()) ::
          {:ok, list_collections_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, list_collections_errors()}
  def list_collections(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "ListCollections", input, options)
  end

  @doc """

  This operation applies only to Amazon Rekognition Custom Labels.

  Lists the entries (images) within a dataset. An entry is a
  JSON Line that contains the information for a single image, including
  the image location, assigned labels, and object location bounding boxes. For
  more information, see [Creating a manifest file](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-manifest-files.html).

  JSON Lines in the response include information about non-terminal
  errors found in the dataset.
  Non terminal errors are reported in `errors` lists within each JSON Line. The
  same information is reported in the training and testing validation result
  manifests that
  Amazon Rekognition Custom Labels creates during model training.

  You can filter the response in variety of ways, such as choosing which labels to
  return and returning JSON Lines created after a specific date.

  This operation requires permissions to perform the
  `rekognition:ListDatasetEntries` action.
  """
  @spec list_dataset_entries(map(), list_dataset_entries_request(), list()) ::
          {:ok, list_dataset_entries_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, list_dataset_entries_errors()}
  def list_dataset_entries(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "ListDatasetEntries", input, options)
  end

  @doc """

  This operation applies only to Amazon Rekognition Custom Labels.

  Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to
  describe images. For more information, see
  [Labeling images](https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-labeling-images.html).

  Lists the labels in a dataset. Amazon Rekognition Custom Labels uses labels to
  describe images. For more information, see Labeling images
  in the *Amazon Rekognition Custom Labels Developer Guide*.
  """
  @spec list_dataset_labels(map(), list_dataset_labels_request(), list()) ::
          {:ok, list_dataset_labels_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, list_dataset_labels_errors()}
  def list_dataset_labels(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "ListDatasetLabels", input, options)
  end

  @doc """
  Returns metadata for faces in the specified collection.

  This metadata
  includes information such as the bounding box coordinates, the confidence (that
  the bounding
  box contains a face), and face ID. For an example, see Listing Faces in a
  Collection in the
  Amazon Rekognition Developer Guide.

  This operation requires permissions to perform the `rekognition:ListFaces`
  action.
  """
  @spec list_faces(map(), list_faces_request(), list()) ::
          {:ok, list_faces_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, list_faces_errors()}
  def list_faces(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "ListFaces", input, options)
  end

  @doc """
  Returns a list of media analysis jobs.

  Results are sorted by `CreationTimestamp` in descending order.
  """
  @spec list_media_analysis_jobs(map(), list_media_analysis_jobs_request(), list()) ::
          {:ok, list_media_analysis_jobs_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, list_media_analysis_jobs_errors()}
  def list_media_analysis_jobs(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "ListMediaAnalysisJobs", input, options)
  end

  @doc """

  This operation applies only to Amazon Rekognition Custom Labels.

  Gets a list of the project policies attached to a project.

  To attach a project policy to a project, call `PutProjectPolicy`. To remove a
  project policy from a project, call `DeleteProjectPolicy`.

  This operation requires permissions to perform the
  `rekognition:ListProjectPolicies` action.
  """
  @spec list_project_policies(map(), list_project_policies_request(), list()) ::
          {:ok, list_project_policies_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, list_project_policies_errors()}
  def list_project_policies(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "ListProjectPolicies", input, options)
  end

  @doc """
  Gets a list of stream processors that you have created with
  `CreateStreamProcessor`.
  """
  @spec list_stream_processors(map(), list_stream_processors_request(), list()) ::
          {:ok, list_stream_processors_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, list_stream_processors_errors()}
  def list_stream_processors(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "ListStreamProcessors", input, options)
  end

  @doc """
  Returns a list of tags in an Amazon Rekognition collection, stream processor, or
  Custom Labels
  model.

  This operation requires permissions to perform the
  `rekognition:ListTagsForResource` action.
  """
  @spec list_tags_for_resource(map(), list_tags_for_resource_request(), list()) ::
          {:ok, list_tags_for_resource_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, list_tags_for_resource_errors()}
  def list_tags_for_resource(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "ListTagsForResource", input, options)
  end

  @doc """
  Returns metadata of the User such as `UserID` in the specified collection.

  Anonymous User (to reserve faces without any identity) is not returned as part
  of this
  request. The results are sorted by system generated primary key ID. If the
  response is
  truncated, `NextToken` is returned in the response that can be used in the
  subsequent request to retrieve the next set of identities.
  """
  @spec list_users(map(), list_users_request(), list()) ::
          {:ok, list_users_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, list_users_errors()}
  def list_users(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "ListUsers", input, options)
  end

  @doc """

  This operation applies only to Amazon Rekognition Custom Labels.

  Attaches a project policy to a Amazon Rekognition Custom Labels project in a
  trusting AWS account. A
  project policy specifies that a trusted AWS account can copy a model version
  from a
  trusting AWS account to a project in the trusted AWS account. To copy a model
  version
  you use the `CopyProjectVersion` operation. Only applies to Custom Labels
  projects.

  For more information about the format of a project policy document, see
  Attaching a project policy (SDK)
  in the *Amazon Rekognition Custom Labels Developer Guide*.

  The response from `PutProjectPolicy` is a revision ID for the project policy.
  You can attach multiple project policies to a project. You can also update an
  existing
  project policy by specifying the policy revision ID of the existing policy.

  To remove a project policy from a project, call `DeleteProjectPolicy`.
  To get a list of project policies attached to a project, call
  `ListProjectPolicies`.

  You copy a model version by calling `CopyProjectVersion`.

  This operation requires permissions to perform the
  `rekognition:PutProjectPolicy` action.
  """
  @spec put_project_policy(map(), put_project_policy_request(), list()) ::
          {:ok, put_project_policy_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, put_project_policy_errors()}
  def put_project_policy(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "PutProjectPolicy", input, options)
  end

  @doc """
  Returns an array of celebrities recognized in the input image.

  For more
  information, see Recognizing celebrities in the Amazon Rekognition Developer
  Guide.

  `RecognizeCelebrities` returns the 64 largest faces in the image. It lists
  the recognized celebrities in the `CelebrityFaces` array and any unrecognized
  faces
  in the `UnrecognizedFaces` array. `RecognizeCelebrities` doesn't return
  celebrities whose faces aren't among the largest 64 faces in the image.

  For each celebrity recognized, `RecognizeCelebrities` returns a
  `Celebrity` object. The `Celebrity` object contains the celebrity
  name, ID, URL links to additional information, match confidence, and a
  `ComparedFace` object that you can use to locate the celebrity's face on the
  image.

  Amazon Rekognition doesn't retain information about which images a celebrity has
  been recognized
  in. Your application must store this information and use the `Celebrity` ID
  property as a unique identifier for the celebrity. If you don't store the
  celebrity name or
  additional information URLs returned by `RecognizeCelebrities`, you will need
  the
  ID to identify the celebrity in a call to the `GetCelebrityInfo`
  operation.

  You pass the input image either as base64-encoded image bytes or as a reference
  to an
  image in an Amazon S3 bucket. If you use the
  AWS
  CLI to call Amazon Rekognition operations, passing image bytes is not
  supported. The image must be either a PNG or JPEG formatted file.

  For an example, see Recognizing celebrities in an image in the Amazon
  Rekognition
  Developer Guide.

  This operation requires permissions to perform the
  `rekognition:RecognizeCelebrities` operation.
  """
  @spec recognize_celebrities(map(), recognize_celebrities_request(), list()) ::
          {:ok, recognize_celebrities_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, recognize_celebrities_errors()}
  def recognize_celebrities(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "RecognizeCelebrities", input, options)
  end

  @doc """
  For a given input face ID, searches for matching faces in the collection the
  face
  belongs to.

  You get a face ID when you add a face to the collection using the `IndexFaces`
  operation. The operation compares the features of the input face with
  faces in the specified collection.

  You can also search faces without indexing faces by using the
  `SearchFacesByImage` operation.

  The operation response returns an array of faces that match, ordered by
  similarity
  score with the highest similarity first. More specifically, it is an array of
  metadata for
  each face match that is found. Along with the metadata, the response also
  includes a
  `confidence` value for each face match, indicating the confidence that the
  specific face matches the input face.

  For an example, see Searching for a face using its face ID in the Amazon
  Rekognition
  Developer Guide.

  This operation requires permissions to perform the `rekognition:SearchFaces`
  action.
  """
  @spec search_faces(map(), search_faces_request(), list()) ::
          {:ok, search_faces_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, search_faces_errors()}
  def search_faces(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "SearchFaces", input, options)
  end

  @doc """
  For a given input image, first detects the largest face in the image, and then
  searches
  the specified collection for matching faces.

  The operation compares the features of the input
  face with faces in the specified collection.

  To search for all faces in an input image, you might first call the `IndexFaces`
  operation, and then use the face IDs returned in subsequent calls
  to the `SearchFaces` operation.

  You can also call the `DetectFaces` operation and use the bounding boxes
  in the response to make face crops, which then you can pass in to the
  `SearchFacesByImage` operation.

  You pass the input image either as base64-encoded image bytes or as a reference
  to an
  image in an Amazon S3 bucket. If you use the
  AWS
  CLI to call Amazon Rekognition operations, passing image bytes is not
  supported. The image must be either a PNG or JPEG formatted file.

  The response returns an array of faces that match, ordered by similarity score
  with
  the highest similarity first. More specifically, it is an array of metadata for
  each face
  match found. Along with the metadata, the response also includes a `similarity`
  indicating how similar the face is to the input face. In the response, the
  operation also
  returns the bounding box (and a confidence level that the bounding box contains
  a face) of the
  face that Amazon Rekognition used for the input image.

  If no faces are detected in the input image, `SearchFacesByImage` returns an
  `InvalidParameterException` error.

  For an example, Searching for a Face Using an Image in the Amazon Rekognition
  Developer Guide.

  The `QualityFilter` input parameter allows you to filter out detected faces
  that don’t meet a required quality bar. The quality bar is based on a variety of
  common use
  cases. Use `QualityFilter` to set the quality bar for filtering by specifying
  `LOW`, `MEDIUM`, or `HIGH`. If you do not want to filter
  detected faces, specify `NONE`. The default value is `NONE`.

  To use quality filtering, you need a collection associated with version 3 of the
  face
  model or higher. To get the version of the face model associated with a
  collection, call
  `DescribeCollection`.

  This operation requires permissions to perform the
  `rekognition:SearchFacesByImage` action.
  """
  @spec search_faces_by_image(map(), search_faces_by_image_request(), list()) ::
          {:ok, search_faces_by_image_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, search_faces_by_image_errors()}
  def search_faces_by_image(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "SearchFacesByImage", input, options)
  end

  @doc """
  Searches for UserIDs within a collection based on a `FaceId` or
  `UserId`.

  This API can be used to find the closest UserID (with a highest
  similarity) to associate a face. The request must be provided with either
  `FaceId`
  or `UserId`. The operation returns an array of UserID that match the
  `FaceId` or `UserId`, ordered by similarity score with the highest
  similarity first.
  """
  @spec search_users(map(), search_users_request(), list()) ::
          {:ok, search_users_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, search_users_errors()}
  def search_users(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "SearchUsers", input, options)
  end

  @doc """
  Searches for UserIDs using a supplied image.

  It first detects the largest face in the
  image, and then searches a specified collection for matching UserIDs.

  The operation returns an array of UserIDs that match the face in the supplied
  image,
  ordered by similarity score with the highest similarity first. It also returns a
  bounding box
  for the face found in the input image.

  Information about faces detected in the supplied image, but not used for the
  search, is
  returned in an array of `UnsearchedFace` objects. If no valid face is detected
  in
  the image, the response will contain an empty `UserMatches` list and no
  `SearchedFace` object.
  """
  @spec search_users_by_image(map(), search_users_by_image_request(), list()) ::
          {:ok, search_users_by_image_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, search_users_by_image_errors()}
  def search_users_by_image(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "SearchUsersByImage", input, options)
  end

  @doc """
  Starts asynchronous recognition of celebrities in a stored video.

  Amazon Rekognition Video can detect celebrities in a video must be stored in an
  Amazon S3 bucket. Use `Video` to specify the bucket name
  and the filename of the video.
  `StartCelebrityRecognition`
  returns a job identifier (`JobId`) which you use to get the results of the
  analysis.
  When celebrity recognition analysis is finished, Amazon Rekognition Video
  publishes a completion status
  to the Amazon Simple Notification Service topic that you specify in
  `NotificationChannel`.
  To get the results of the celebrity recognition analysis, first check that the
  status value published to the Amazon SNS
  topic is `SUCCEEDED`. If so, call `GetCelebrityRecognition` and pass the job
  identifier
  (`JobId`) from the initial call to `StartCelebrityRecognition`.

  For more information, see Recognizing celebrities in the Amazon Rekognition
  Developer Guide.
  """
  @spec start_celebrity_recognition(map(), start_celebrity_recognition_request(), list()) ::
          {:ok, start_celebrity_recognition_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, start_celebrity_recognition_errors()}
  def start_celebrity_recognition(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartCelebrityRecognition", input, options)
  end

  @doc """
  Starts asynchronous detection of inappropriate, unwanted, or offensive content
  in a stored video.

  For a list of moderation labels in Amazon Rekognition, see
  [Using the image and video moderation APIs](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html#moderation-api).

  Amazon Rekognition Video can moderate content in a video stored in an Amazon S3
  bucket. Use `Video` to specify the bucket name
  and the filename of the video. `StartContentModeration`
  returns a job identifier (`JobId`) which you use to get the results of the
  analysis.
  When content analysis is finished, Amazon Rekognition Video publishes a
  completion status
  to the Amazon Simple Notification Service topic that you specify in
  `NotificationChannel`.

  To get the results of the content analysis, first check that the status value
  published to the Amazon SNS
  topic is `SUCCEEDED`. If so, call `GetContentModeration` and pass the job
  identifier
  (`JobId`) from the initial call to `StartContentModeration`.

  For more information, see Moderating content in the Amazon Rekognition Developer
  Guide.
  """
  @spec start_content_moderation(map(), start_content_moderation_request(), list()) ::
          {:ok, start_content_moderation_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, start_content_moderation_errors()}
  def start_content_moderation(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartContentModeration", input, options)
  end

  @doc """
  Starts asynchronous detection of faces in a stored video.

  Amazon Rekognition Video can detect faces in a video stored in an Amazon S3
  bucket.
  Use `Video` to specify the bucket name and the filename of the video.
  `StartFaceDetection` returns a job identifier (`JobId`) that you
  use to get the results of the operation.
  When face detection is finished, Amazon Rekognition Video publishes a completion
  status
  to the Amazon Simple Notification Service topic that you specify in
  `NotificationChannel`.
  To get the results of the face detection operation, first check that the status
  value published to the Amazon SNS
  topic is `SUCCEEDED`. If so, call `GetFaceDetection` and pass the job identifier
  (`JobId`) from the initial call to `StartFaceDetection`.

  For more information, see Detecting faces in a stored video in the
  Amazon Rekognition Developer Guide.
  """
  @spec start_face_detection(map(), start_face_detection_request(), list()) ::
          {:ok, start_face_detection_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, start_face_detection_errors()}
  def start_face_detection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartFaceDetection", input, options)
  end

  @doc """
  Starts the asynchronous search for faces in a collection that match the faces of
  persons detected in a stored video.

  The video must be stored in an Amazon S3 bucket. Use `Video` to specify the
  bucket name
  and the filename of the video. `StartFaceSearch`
  returns a job identifier (`JobId`) which you use to get the search results once
  the search has completed.
  When searching is finished, Amazon Rekognition Video publishes a completion
  status
  to the Amazon Simple Notification Service topic that you specify in
  `NotificationChannel`.
  To get the search results, first check that the status value published to the
  Amazon SNS
  topic is `SUCCEEDED`. If so, call `GetFaceSearch` and pass the job identifier
  (`JobId`) from the initial call to `StartFaceSearch`. For more information, see
  [Searching stored videos for faces](https://docs.aws.amazon.com/rekognition/latest/dg/procedure-person-search-videos.html).
  """
  @spec start_face_search(map(), start_face_search_request(), list()) ::
          {:ok, start_face_search_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, start_face_search_errors()}
  def start_face_search(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartFaceSearch", input, options)
  end

  @doc """
  Starts asynchronous detection of labels in a stored video.

  Amazon Rekognition Video can detect labels in a video. Labels are instances of
  real-world entities.
  This includes objects like flower, tree, and table; events like
  wedding, graduation, and birthday party; concepts like landscape, evening, and
  nature; and activities
  like a person getting out of a car or a person skiing.

  The video must be stored in an Amazon S3 bucket. Use `Video` to specify the
  bucket name
  and the filename of the video.
  `StartLabelDetection` returns a job identifier (`JobId`) which you use to get
  the
  results of the operation. When label detection is finished, Amazon Rekognition
  Video publishes a completion status
  to the Amazon Simple Notification Service topic that you specify in
  `NotificationChannel`.

  To get the results of the label detection operation, first check that the status
  value published to the Amazon SNS
  topic is `SUCCEEDED`. If so, call `GetLabelDetection` and pass the job
  identifier
  (`JobId`) from the initial call to `StartLabelDetection`.

  *Optional Parameters*

  `StartLabelDetection` has the `GENERAL_LABELS` Feature applied by
  default. This feature allows you to provide filtering criteria to the `Settings`
  parameter. You can filter with sets of individual labels or with label
  categories. You can
  specify inclusive filters, exclusive filters, or a combination of inclusive and
  exclusive
  filters. For more information on filtering, see [Detecting labels in a video](https://docs.aws.amazon.com/rekognition/latest/dg/labels-detecting-labels-video.html).

  You can specify `MinConfidence` to control the confidence threshold for the
  labels returned. The default is 50.
  """
  @spec start_label_detection(map(), start_label_detection_request(), list()) ::
          {:ok, start_label_detection_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, start_label_detection_errors()}
  def start_label_detection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartLabelDetection", input, options)
  end

  @doc """
  Initiates a new media analysis job.

  Accepts a manifest file in an Amazon S3 bucket. The
  output is a manifest file and a summary of the manifest stored in the Amazon S3
  bucket.
  """
  @spec start_media_analysis_job(map(), start_media_analysis_job_request(), list()) ::
          {:ok, start_media_analysis_job_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, start_media_analysis_job_errors()}
  def start_media_analysis_job(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartMediaAnalysisJob", input, options)
  end

  @doc """


  *End of support notice:* On October 31, 2025, AWS will discontinue
  support for Amazon Rekognition People Pathing.

  After October 31, 2025, you will no
  longer be able to use the Rekognition People Pathing capability. For more
  information,
  visit this [blog post](https://aws.amazon.com/blogs/machine-learning/transitioning-from-amazon-rekognition-people-pathing-exploring-other-alternatives/).

  Starts the asynchronous tracking of a person's path in a stored video.

  Amazon Rekognition Video can track the path of people in a video stored in an
  Amazon S3 bucket. Use `Video` to specify the bucket name
  and the filename of the video. `StartPersonTracking`
  returns a job identifier (`JobId`) which you use to get the results of the
  operation.
  When label detection is finished, Amazon Rekognition publishes a completion
  status
  to the Amazon Simple Notification Service topic that you specify in
  `NotificationChannel`.

  To get the results of the person detection operation, first check that the
  status value published to the Amazon SNS
  topic is `SUCCEEDED`. If so, call `GetPersonTracking` and pass the job
  identifier
  (`JobId`) from the initial call to `StartPersonTracking`.
  """
  @spec start_person_tracking(map(), start_person_tracking_request(), list()) ::
          {:ok, start_person_tracking_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, start_person_tracking_errors()}
  def start_person_tracking(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartPersonTracking", input, options)
  end

  @doc """

  This operation applies only to Amazon Rekognition Custom Labels.

  Starts the running of the version of a model. Starting a model takes a while to
  complete. To check the current state of the model, use
  `DescribeProjectVersions`.

  Once the model is running, you can detect custom labels in new images by calling
  `DetectCustomLabels`.

  You are charged for the amount of time that the model is running. To stop a
  running
  model, call `StopProjectVersion`.

  This operation requires permissions to perform the
  `rekognition:StartProjectVersion` action.
  """
  @spec start_project_version(map(), start_project_version_request(), list()) ::
          {:ok, start_project_version_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, start_project_version_errors()}
  def start_project_version(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartProjectVersion", input, options)
  end

  @doc """
  Starts asynchronous detection of segment detection in a stored video.

  Amazon Rekognition Video can detect segments in a video stored in an Amazon S3
  bucket. Use `Video` to specify the bucket name and
  the filename of the video. `StartSegmentDetection` returns a job identifier
  (`JobId`) which you use to get
  the results of the operation. When segment detection is finished, Amazon
  Rekognition Video publishes a completion status to the Amazon Simple
  Notification Service topic
  that you specify in `NotificationChannel`.

  You can use the `Filters` (`StartSegmentDetectionFilters`)
  input parameter to specify the minimum detection confidence returned in the
  response.
  Within `Filters`, use `ShotFilter` (`StartShotDetectionFilter`)
  to filter detected shots. Use `TechnicalCueFilter`
  (`StartTechnicalCueDetectionFilter`)
  to filter technical cues.

  To get the results of the segment detection operation, first check that the
  status value published to the Amazon SNS
  topic is `SUCCEEDED`. if so, call `GetSegmentDetection` and pass the job
  identifier (`JobId`)
  from the initial call to `StartSegmentDetection`.

  For more information, see Detecting video segments in stored video in the Amazon
  Rekognition Developer Guide.
  """
  @spec start_segment_detection(map(), start_segment_detection_request(), list()) ::
          {:ok, start_segment_detection_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, start_segment_detection_errors()}
  def start_segment_detection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartSegmentDetection", input, options)
  end

  @doc """
  Starts processing a stream processor.

  You create a stream processor by calling `CreateStreamProcessor`.
  To tell `StartStreamProcessor` which stream processor to start, use the value of
  the `Name` field specified in the call to
  `CreateStreamProcessor`.

  If you are using a label detection stream processor to detect labels, you need
  to provide a `Start selector` and a `Stop selector` to determine the length of
  the stream processing time.
  """
  @spec start_stream_processor(map(), start_stream_processor_request(), list()) ::
          {:ok, start_stream_processor_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, start_stream_processor_errors()}
  def start_stream_processor(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartStreamProcessor", input, options)
  end

  @doc """
  Starts asynchronous detection of text in a stored video.

  Amazon Rekognition Video can detect text in a video stored in an Amazon S3
  bucket. Use `Video` to specify the bucket name and
  the filename of the video. `StartTextDetection` returns a job identifier
  (`JobId`) which you use to get
  the results of the operation. When text detection is finished, Amazon
  Rekognition Video publishes a completion status to the Amazon Simple
  Notification Service topic
  that you specify in `NotificationChannel`.

  To get the results of the text detection operation, first check that the status
  value published to the Amazon SNS
  topic is `SUCCEEDED`. if so, call `GetTextDetection` and pass the job identifier
  (`JobId`)
  from the initial call to `StartTextDetection`.
  """
  @spec start_text_detection(map(), start_text_detection_request(), list()) ::
          {:ok, start_text_detection_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, start_text_detection_errors()}
  def start_text_detection(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StartTextDetection", input, options)
  end

  @doc """

  This operation applies only to Amazon Rekognition Custom Labels.

  Stops a running model. The operation might take a while to complete. To check
  the
  current status, call `DescribeProjectVersions`. Only applies to Custom
  Labels projects.

  This operation requires permissions to perform the
  `rekognition:StopProjectVersion` action.
  """
  @spec stop_project_version(map(), stop_project_version_request(), list()) ::
          {:ok, stop_project_version_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, stop_project_version_errors()}
  def stop_project_version(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StopProjectVersion", input, options)
  end

  @doc """
  Stops a running stream processor that was created by `CreateStreamProcessor`.
  """
  @spec stop_stream_processor(map(), stop_stream_processor_request(), list()) ::
          {:ok, stop_stream_processor_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, stop_stream_processor_errors()}
  def stop_stream_processor(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "StopStreamProcessor", input, options)
  end

  @doc """
  Adds one or more key-value tags to an Amazon Rekognition collection, stream
  processor, or Custom
  Labels model.

  For more information, see [Tagging AWS Resources](https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html).

  This operation requires permissions to perform the `rekognition:TagResource`
  action.
  """
  @spec tag_resource(map(), tag_resource_request(), list()) ::
          {:ok, tag_resource_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, tag_resource_errors()}
  def tag_resource(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "TagResource", input, options)
  end

  @doc """
  Removes one or more tags from an Amazon Rekognition collection, stream
  processor, or Custom Labels
  model.

  This operation requires permissions to perform the
  `rekognition:UntagResource` action.
  """
  @spec untag_resource(map(), untag_resource_request(), list()) ::
          {:ok, untag_resource_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, untag_resource_errors()}
  def untag_resource(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "UntagResource", input, options)
  end

  @doc """

  This operation applies only to Amazon Rekognition Custom Labels.

  Adds or updates one or more entries (images) in a dataset. An entry is a JSON
  Line which contains the
  information for a single image, including
  the image location, assigned labels, and object location bounding boxes. For
  more information,
  see Image-Level labels in manifest files and Object localization in manifest
  files in the *Amazon Rekognition Custom Labels Developer Guide*.

  If the `source-ref` field in the JSON line references an existing image, the
  existing image in the dataset
  is updated.
  If `source-ref` field doesn't reference an existing image, the image is added as
  a new image to the dataset.

  You specify the changes that you want to make in the `Changes` input parameter.
  There isn't a limit to the number JSON Lines that you can change, but the size
  of `Changes` must be less
  than 5MB.

  `UpdateDatasetEntries` returns immediatly, but the dataset update might take a
  while to complete.
  Use `DescribeDataset` to check the
  current status. The dataset updated successfully if the value of `Status` is
  `UPDATE_COMPLETE`.

  To check if any non-terminal errors occured, call `ListDatasetEntries`
  and check for the presence of `errors` lists in the JSON Lines.

  Dataset update fails if a terminal error occurs (`Status` = `UPDATE_FAILED`).
  Currently, you can't access the terminal error information from the Amazon
  Rekognition Custom Labels SDK.

  This operation requires permissions to perform the
  `rekognition:UpdateDatasetEntries` action.
  """
  @spec update_dataset_entries(map(), update_dataset_entries_request(), list()) ::
          {:ok, update_dataset_entries_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, update_dataset_entries_errors()}
  def update_dataset_entries(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "UpdateDatasetEntries", input, options)
  end

  @doc """

  Allows you to update a stream processor.

  You can change some settings and regions of interest and delete certain
  parameters.
  """
  @spec update_stream_processor(map(), update_stream_processor_request(), list()) ::
          {:ok, update_stream_processor_response(), any()}
          | {:error, {:unexpected_response, any()}}
          | {:error, term()}
          | {:error, update_stream_processor_errors()}
  def update_stream_processor(%Client{} = client, input, options \\ []) do
    meta = metadata()

    Request.request_post(client, meta, "UpdateStreamProcessor", input, options)
  end
end
