async fn create_trial_component(
        &self,
        input: CreateTrialComponentRequest,
    ) -> Result<CreateTrialComponentResponse, RusotoError<CreateTrialComponentError>> {
        let mut request = self.new_signed_request("POST", "/");
        request.add_header("x-amz-target", "SageMaker.CreateTrialComponent");
        let encoded = serde_json::to_string(&input).unwrap();
        request.set_payload(Some(encoded));

        let response = self
            .sign_and_dispatch(request, CreateTrialComponentError::from_response)
            .await?;
        let mut response = response;
        let response = response.buffer().await.map_err(RusotoError::HttpDispatch)?;
        proto::json::ResponsePayload::new(&response)
            .deserialize::<CreateTrialComponentResponse, _>()
    }
<p><p>Creates a <i>trial component</i>, which is a stage of a machine learning <i>trial</i>. A trial is composed of one or more trial components. A trial component can be used in multiple trials.</p> <p>Trial components include pre-processing jobs, training jobs, and batch transform jobs.</p> <p>When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.</p> <p>You can add tags to a trial component and then use the <a>Search</a> API to search for the tags.</p> <note> <p> <code>CreateTrialComponent</code> can only be invoked from within an SageMaker managed environment. This includes SageMaker training jobs, processing jobs, transform jobs, and SageMaker notebooks. A call to <code>CreateTrialComponent</code> from outside one of these environments results in an error.</p> </note></p>
rusoto/services/sagemaker/src/generated.rs