code: def get_structured_inputs_from_func_graph(
      func_graph: FuncGraph,
  ) -> Mapping[str, common_types.TensorType]:
      """Get structured inputs to a FuncGraph.
  
      Args:
        func_graph: A `FuncGraph` object.
  
      Returns:
        Input graph tensors of `func_graph` formatted as possibly-nested python
        objects received by it.
      """
      # structured_input_signature is a tuple of (args, kwargs). [0][0] retrieves
      # the structure of the first arg, which for the preprocessing function is
      # the dictionary of features.
      input_signature = func_graph.structured_input_signature[0][0]
      num_captures = len(
          func_graph.internal_captures
          + func_graph.deferred_internal_captures
      )
      # `func_graph.inputs` contains placeholders that represent regular inputs
      # followed by captured inputs. We are only interested in the regular inputs.
      graph_inputs = copy.copy(func_graph.inputs)
      if num_captures > 0:
          graph_inputs = graph_inputs[:-num_captures]
      return tf.nest.pack_sequence_as(
          input_signature, graph_inputs, expand_composites=True
      )
  
file_path: transform/tensorflow_transform/tf2_utils.py
imports: import copy
  import itertools
  from typing import Collection, Iterable, Mapping, Optional, Tuple
  import tensorflow
  from tensorflow_transform import common_types
  from tensorflow.python import tf2
  from tensorflow.python.framework.func_graph import FuncGraph
label_desc: Use image node_id 5 for calling a global function with example usage: get_structured_inputs_from_func_graph(func_graph) and returns: tf