code: def _get_output_to_inputs_map(
      output_signature: Mapping[str, common_types.TensorType]
  ) -> Dict[str, Iterable[tf.Tensor]]:
      """Gets all graph inputs that the tensors in output_signature depend on."""
      result = {}
      for name, output in output_signature.items():
          components = _get_component_tensors(output)
          sinks = [_as_operation(component) for component in components]
          # Ignore control dependencies when walking the graph as we only care about
          # which user defined inputs this output depends on.
          result[name] = graph_tools.retrieve_sources(
              sinks, ignore_control_dependencies=True
          )
      return result
  
imports: from typing import Any, Dict, Iterable, Mapping, Tuple, Union
  import tensorflow
  from tensorflow_transform import annotators
  from tensorflow_transform import common_types
  from tensorflow_transform import graph_tools
  from tensorflow_transform import tf2_utils
  from tensorflow_transform import tf_utils
  from tensorflow_transform.py_func import pyfunc_helper
  from tensorflow_transform.saved import constants
  from tensorflow_transform.saved import saved_model_loader
  from tensorflow_transform.saved import saved_transform_io
  from tensorflow.python.eager import function
  from tensorflow.python.eager import wrap_function
  from tensorflow.python.framework import composite_tensor
  from tensorflow.python.ops import lookup_ops
  from tensorflow.python.util import object_identity
label_desc: Use image node_id 4 for calling a global function with example usage: _get_output_to_inputs_map(output_signature) and returns: result