class_docstr: TrainingRange operator.
class_docstr_tok: ["TrainingRange","operator","."]
docstr: Returns the Examples artifacts used to train the Model.
docstr_tok: ["Returns","the","Examples","artifacts","used","to","train","the","Model","."]
code: def apply(
      self, input_list: Sequence[types.Artifact]
  ) -> Sequence[types.Artifact]:
      """Returns the Examples artifacts used to train the Model."""
      if not input_list:
          return []
  
      model = _validate_input_list(input_list)
  
      examples = training_range(
          self.context.store, model, self.use_transformed_examples
      )
  
      if not examples:
          return []
  
      return examples
  
file_path: tfx/tfx/dsl/input_resolution/ops/training_range_op.py
imports: from typing import Any, List, Sequence
  from tfx import types
  from tfx.dsl.input_resolution import resolver_op
  from tfx.dsl.input_resolution.ops import ops_utils
  from tfx.orchestration.portable.input_resolution import exceptions
  from tfx.orchestration.portable.mlmd import event_lib
  from tfx.types import artifact_utils
  from ml_metadata.proto import metadata_store_pb2
  from ml_metadata.tools.mlmd_resolver import metadata_resolver
label_desc: Use image node_id 1 for calling the TrainingRange obj's underlying member method code with example usage: obj.apply(input_list) and returns: examples, list, list