code: def test_json_type_data_config_missing_features():
      # facets in input dataset
      s3_data_input_path = "s3://path/to/input.csv"
      s3_output_path = "s3://path/to/output"
      label_name = "Label"
      headers = ["Label", "F1", "F2", "F3", "F4", "Predicted Label"]
      with pytest.raises(
          ValueError,
          match="features JMESPath is required for application/json dataset_type",
      ):
          DataConfig(
              s3_data_input_path=s3_data_input_path,
              s3_output_path=s3_output_path,
              features=None,
              label=label_name,
              headers=headers,
              dataset_type="application/json",
              excluded_columns=["F4"],
              predicted_label="Predicted Label",
          )
  
imports: from __future__ import absolute_import, print_function
  import copy
  import pytest
  from mock import MagicMock, Mock, patch
  from sagemaker import Processor, image_uris
  from sagemaker.clarify import BiasConfig, DataConfig, ModelConfig, ModelPredictedLabelConfig, PDPConfig, SageMakerClarifyProcessor, SHAPConfig, TextConfig, ImageConfig, _AnalysisConfigGenerator, DatasetType, ProcessingOutputHandler, SegmentationConfig
label_desc: Use image node_id 7 for calling a global function with example usage: test_json_type_data_config_missing_features() without return types