code: def test_auto_ml_validation_channel_name(sagemaker_session):
      auto_ml = AutoML(
          role=ROLE,
          target_attribute_name=TARGET_ATTRIBUTE_NAME,
          sagemaker_session=sagemaker_session,
      )
      input_training = AutoMLInput(
          inputs=DEFAULT_S3_INPUT_DATA,
          target_attribute_name="target",
          compression="Gzip",
          channel_type="training",
          sample_weight_attribute_name="sampleWeight",
      )
      input_validation = AutoMLInput(
          inputs=DEFAULT_S3_VALIDATION_DATA,
          target_attribute_name="target",
          sample_weight_attribute_name="sampleWeight",
          compression="Gzip",
          channel_type="validation",
      )
      inputs = [input_training, input_validation]
      AutoMLJob.start_new(auto_ml, inputs)
      sagemaker_session.auto_ml.assert_called_once()
      _, args = sagemaker_session.auto_ml.call_args
      assert args["input_config"] == [
          {
              "ChannelType": "training",
              "CompressionType": "Gzip",
              "DataSource": {
                  "S3DataSource": {
                      "S3DataType": "S3Prefix",
                      "S3Uri": DEFAULT_S3_INPUT_DATA,
                  }
              },
              "TargetAttributeName": TARGET_ATTRIBUTE_NAME,
              "SampleWeightAttributeName": SAMPLE_WEIGHT_ATTRIBUTE_NAME,
          },
          {
              "ChannelType": "validation",
              "CompressionType": "Gzip",
              "DataSource": {
                  "S3DataSource": {
                      "S3DataType": "S3Prefix",
                      "S3Uri": DEFAULT_S3_VALIDATION_DATA,
                  }
              },
              "TargetAttributeName": TARGET_ATTRIBUTE_NAME,
              "SampleWeightAttributeName": SAMPLE_WEIGHT_ATTRIBUTE_NAME,
          },
      ]
  
imports: from __future__ import absolute_import
  import copy
  import pytest
  from mock import Mock, patch
  from sagemaker import AutoML, AutoMLJob, AutoMLInput, CandidateEstimator, PipelineModel
  from sagemaker.predictor import Predictor
  from sagemaker.session_settings import SessionSettings
  from sagemaker.workflow.functions import Join
  from tests.unit import SAGEMAKER_CONFIG_AUTO_ML, SAGEMAKER_CONFIG_TRAINING_JOB, _test_default_bucket_and_prefix_combinations, DEFAULT_S3_BUCKET_NAME, DEFAULT_S3_OBJECT_KEY_PREFIX_NAME
label_desc: Use image node_id 7 for calling a global function with example usage: test_auto_ml_validation_channel_name(sagemaker_session) without return types