# This file is auto-generated by AWSMetadata.jl
using AWS
using AWS.AWSServices: sagemaker_runtime
using AWS.Compat
using AWS.UUIDs

"""
    invoke_endpoint(body, endpoint_name)
    invoke_endpoint(body, endpoint_name, params::Dict{String,<:Any})

After you deploy a model into production using Amazon SageMaker hosting services, your
client applications use this API to get inferences from the model hosted at the specified
endpoint.  For an overview of Amazon SageMaker, see How It Works.  Amazon SageMaker strips
all POST headers except those supported by the API. Amazon SageMaker might add additional
headers. You should not rely on the behavior of headers outside those enumerated in the
request syntax.  Calls to InvokeEndpoint are authenticated by using AWS Signature Version
4. For information, see Authenticating Requests (AWS Signature Version 4) in the Amazon S3
API Reference. A customer's model containers must respond to requests within 60 seconds.
The model itself can have a maximum processing time of 60 seconds before responding to
invocations. If your model is going to take 50-60 seconds of processing time, the SDK
socket timeout should be set to be 70 seconds.  Endpoints are scoped to an individual
account, and are not public. The URL does not contain the account ID, but Amazon SageMaker
determines the account ID from the authentication token that is supplied by the caller.

# Arguments
- `body`: Provides input data, in the format specified in the ContentType request header.
  Amazon SageMaker passes all of the data in the body to the model.  For information about
  the format of the request body, see Common Data Formats-Inference.
- `endpoint_name`: The name of the endpoint that you specified when you created the
  endpoint using the CreateEndpoint API.

# Optional Parameters
Optional parameters can be passed as a `params::Dict{String,<:Any}`. Valid keys are:
- `"Accept"`: The desired MIME type of the inference in the response.
- `"Content-Type"`: The MIME type of the input data in the request body.
- `"X-Amzn-SageMaker-Custom-Attributes"`: Provides additional information about a request
  for an inference submitted to a model hosted at an Amazon SageMaker endpoint. The
  information is an opaque value that is forwarded verbatim. You could use this value, for
  example, to provide an ID that you can use to track a request or to provide other metadata
  that a service endpoint was programmed to process. The value must consist of no more than
  1024 visible US-ASCII characters as specified in Section 3.3.6. Field Value Components of
  the Hypertext Transfer Protocol (HTTP/1.1).  The code in your model is responsible for
  setting or updating any custom attributes in the response. If your code does not set this
  value in the response, an empty value is returned. For example, if a custom attribute
  represents the trace ID, your model can prepend the custom attribute with Trace ID: in your
  post-processing function. This feature is currently supported in the AWS SDKs but not in
  the Amazon SageMaker Python SDK.
- `"X-Amzn-SageMaker-Inference-Id"`: If you provide a value, it is added to the captured
  data when you enable data capture on the endpoint. For information about data capture, see
  Capture Data.
- `"X-Amzn-SageMaker-Target-Container-Hostname"`: If the endpoint hosts multiple containers
  and is configured to use direct invocation, this parameter specifies the host name of the
  container to invoke.
- `"X-Amzn-SageMaker-Target-Model"`: The model to request for inference when invoking a
  multi-model endpoint.
- `"X-Amzn-SageMaker-Target-Variant"`: Specify the production variant to send the inference
  request to when invoking an endpoint that is running two or more variants. Note that this
  parameter overrides the default behavior for the endpoint, which is to distribute the
  invocation traffic based on the variant weights. For information about how to use variant
  targeting to perform a/b testing, see Test models in production
"""
invoke_endpoint(Body, EndpointName; aws_config::AbstractAWSConfig=global_aws_config()) = sagemaker_runtime("POST", "/endpoints/$(EndpointName)/invocations", Dict{String, Any}("Body"=>Body); aws_config=aws_config)
invoke_endpoint(Body, EndpointName, params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()) = sagemaker_runtime("POST", "/endpoints/$(EndpointName)/invocations", Dict{String, Any}(mergewith(_merge, Dict{String, Any}("Body"=>Body), params)); aws_config=aws_config)
