code: def to_nest_placeholder(
      nested_tensor_specs, default=None, name_scope="", outer_dims=()
  ):
      """Converts a nest of TensorSpecs to a nest of matching placeholders.
  
      Args:
        nested_tensor_specs: A nest of tensor specs.
        default: Optional constant value to set as a default for the placeholder.
        name_scope: String name for the scope to create the placeholders in.
        outer_dims: Optional leading dimensions for the placeholder.
  
      Returns:
        A nest of placeholders matching the given tensor spec.
  
      Raises:
        ValueError: If a default is provided outside of the allowed types, or if
          default is a np.array that does not match the spec shape.
      """
      if default is None:
          to_ph = lambda spec: to_placeholder(
              spec, outer_dims=outer_dims
          )
      else:
          if not isinstance(default, (int, float, np.ndarray)):
              raise ValueError(
                  "to_nest_placeholder default value must be an int, "
                  "float, or np.ndarray"
              )
  
          def to_ph(spec):
              shape = list(outer_dims) + spec.shape.as_list()
              if (
                  isinstance(default, np.ndarray)
                  and list(default.shape) != shape
              ):
                  raise ValueError(
                      "Shape mismatch between default value and spec. "
                      "Got {}, expected {}".format(default.shape, shape)
                  )
              const = tf.constant(
                  default, shape=shape, dtype=spec.dtype
              )
              return to_placeholder_with_default(
                  const, spec, outer_dims=outer_dims
              )
  
      with tf.name_scope(name_scope):
          return tf.nest.map_structure(to_ph, nested_tensor_specs)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from typing import Union
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.specs import array_spec
  from tf_agents.typing import types
  from google.protobuf import text_format
  from tensorflow.core.protobuf import struct_pb2
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.saved_model import nested_structure_coder
label_desc: Use image node_id 9 for calling a global function with example usage: to_nest_placeholder(nested_tensor_specs, default, name_scope, outer_dims) and returns: tf, to_placeholder_with_default