text: # -*- coding: utf-8 -*-
  # Copyright 2020 Google LLC
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  # Generated code. DO NOT EDIT!
  #
  # Snippet for CreateHyperparameterTuningJob
  # NOTE: This snippet has been automatically generated for illustrative purposes only.
  # It may require modifications to work in your environment.
  
  # To install the latest published package dependency, execute the following:
  #   python3 -m pip install google-cloud-aiplatform
  
  
  # [START aiplatform_generated_aiplatform_v1_JobService_CreateHyperparameterTuningJob_sync]
  from google.cloud import aiplatform_v1
  
  
  def sample_create_hyperparameter_tuning_job():
      # Create a client
      client = aiplatform_v1.JobServiceClient()
  
      # Initialize request argument(s)
      hyperparameter_tuning_job = aiplatform_v1.HyperparameterTuningJob()
      hyperparameter_tuning_job.display_name = "display_name_value"
      hyperparameter_tuning_job.study_spec.metrics.metric_id = "metric_id_value"
      hyperparameter_tuning_job.study_spec.metrics.goal = "MINIMIZE"
      hyperparameter_tuning_job.study_spec.parameters.double_value_spec.min_value = 0.96
      hyperparameter_tuning_job.study_spec.parameters.double_value_spec.max_value = 0.962
      hyperparameter_tuning_job.study_spec.parameters.parameter_id = "parameter_id_value"
      hyperparameter_tuning_job.max_trial_count = 1609
      hyperparameter_tuning_job.parallel_trial_count = 2128
      hyperparameter_tuning_job.trial_job_spec.worker_pool_specs.container_spec.image_uri = "image_uri_value"
  
      request = aiplatform_v1.CreateHyperparameterTuningJobRequest(
          parent="parent_value",
          hyperparameter_tuning_job=hyperparameter_tuning_job,
      )
  
      # Make the request
      response = client.create_hyperparameter_tuning_job(request=request)
  
      # Handle the response
      print(response)
  
  # [END aiplatform_generated_aiplatform_v1_JobService_CreateHyperparameterTuningJob_sync]
  