code: def _make_inputs_match(branch_graphs, branch_inputs):
      """Modifies branch_graphs so they have the same input signature.
  
      This method reorders and/or adds parameters to each graph in branch_graphs so
      they have the same input signature, and updates the 'inputs' and 'captured'
      fields of each graph accordingly. It uses the input tensors from the outer
      graph to avoid duplicating shared arguments.
  
      Args:
        branch_graphs: a `list` of `FuncGraph`
        branch_inputs: a `list` of `list`s of `Tensor`s in the outer graph. The
          inputs for the corresponding graph in `branch_graphs`.
  
      Returns:
        A new list of Tensors from the outer graph that are the new inputs for each
        branch_graph. This is a deduped version of `sum(branch_inputs)`.
      """
      assert len(branch_graphs) == len(branch_inputs)
      added_inputs = set()
      new_inputs = []
      for branch_in in branch_inputs:
          for tensor in branch_in:
              tensor_id = ops.tensor_id(tensor)
              if tensor_id not in added_inputs:
                  added_inputs.add(tensor_id)
                  new_inputs.append(tensor)
  
      for branch_graph, branch_in in zip(branch_graphs, branch_inputs):
          input_ids = [ops.tensor_id(t) for t in branch_in]
          branch_input_to_param = dict(
              zip(input_ids, branch_graph.inputs)
          )
          input_list = []
          for in_t in new_inputs:
              param = branch_input_to_param.get(ops.tensor_id(in_t))
              if param is None:
                  param = _create_dummy_input(branch_graph, in_t)
              input_list.append(param)
  
          branch_graph.inputs = input_list
  
          # Rewrite the FuncGraphs' state to reflect the new inputs.
          branch_graph.function_captures.reset_captures(
              new_inputs, branch_graph.inputs
          )
  
      return new_inputs
  
imports: import collections
  from tensorflow.core.framework import types_pb2
  from tensorflow.python.eager import backprop_util
  from tensorflow.python.framework import auto_control_deps
  from tensorflow.python.framework import auto_control_deps_utils
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors_impl
  from tensorflow.python.framework import func_graph
  from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import none_tensor
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.framework import type_spec
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_util
  from tensorflow.python.ops import control_flow_util_v2
  from tensorflow.python.ops import default_gradient
  from tensorflow.python.ops import gen_functional_ops
  from tensorflow.python.ops import gen_optional_ops
  from tensorflow.python.ops import gradients_util
  from tensorflow.python.ops import handle_data_util
  from tensorflow.python.ops import math_ops
  from tensorflow.python.util import nest
label_desc: Use image node_id 15 for calling a global function with example usage: _make_inputs_match(branch_graphs, branch_inputs) and returns: new_inputs