code: def merge_to_parameters_from_dict(
      value: Params, params_dict: Mapping[Text, Any]
  ) -> Params:
      """Merges dict matching data of `parameters_to_dict(value)` to a new `Params`.
  
      For more details, see the example below and the documentation of
      `parameters_to_dict`.
  
      Example:
  
      ```python
      scale_matrix = tf.Variable([[1.0, 2.0], [-1.0, 0.0]])
      d = tfp.distributions.MultivariateNormalDiag(
          loc=[1.0, 1.0], scale_diag=[2.0, 3.0], validate_args=True)
      b = tfp.bijectors.ScaleMatvecLinearOperator(
          scale=tf.linalg.LinearOperatorFullMatrix(matrix=scale_matrix),
          adjoint=True)
      b_d = b(d)
      p = utils.get_parameters(b_d)
  
      params_dict = utils.parameters_to_dict(p)
      params_dict["bijector"]["scale"]["matrix"] = new_scale_matrix
  
      new_params = utils.merge_to_parameters_from_dict(
        p, params_dict)
  
      # new_d is a `ScaleMatvecLinearOperator()(MultivariateNormalDiag)` with
      # a new scale matrix.
      new_d = utils.make_from_parameters(new_params)
      ```
  
      Args:
        value: A `Params` from which `params_dict` was derived.
        params_dict: A nested `dict` created by e.g. calling
          `parameters_to_dict(value)` and  modifying it to modify parameters.
          **NOTE** If any keys in the dict are missing, the "default" value in
          `value` is used instead.
  
      Returns:
        A new `Params` object which can then be turned into e.g. a
        `tfp.Distribution` via `make_from_parameters`.
  
      Raises:
        ValueError: If `params_dict` has keys missing from `value.params`.
        KeyError: If a subdict entry is missing for a nested value in
          `value.params`.
      """
      new_params = {}
      if params_dict is None:
          params_dict = {}
  
      processed_params = set()
      for k, v in value.params.items():
          # pylint: disable=cell-var-from-loop
          visited = set()
          converted = set()
  
          def convert(params_k, p):
              if params_k is not None:
                  params_key = "{}:{}".format(k, params_k)
                  visited.add(params_key)
                  params_dict_value = params_dict.get(params_key, None)
                  if params_dict_value is not None:
                      converted.add(params_key)
              else:
                  params_key = k
                  params_dict_value = params_dict.get(k, None)
              processed_params.add(params_key)
              if isinstance(p, Params):
                  return merge_to_parameters_from_dict(
                      p, params_dict_value
                  )
              else:
                  return (
                      params_dict_value
                      if params_dict_value is not None
                      else p
                  )
  
          # pylint: enable=cell-var-from-loop
  
          if tf.nest.is_nested(v):
              new_params[k] = nest_utils.map_structure_with_paths(
                  convert, v
              )
              if converted and visited != converted:
                  raise KeyError(
                      "Only saw partial information from the dictionary for nested "
                      "key '{}' in params_dict.  Entries provided: {}.  "
                      "Entries required: {}".format(
                          k, sorted(converted), sorted(visited)
                      )
                  )
          else:
              new_params[k] = convert(None, v)
  
      unvisited_params_keys = set(params_dict) - processed_params
      if unvisited_params_keys:
          raise ValueError(
              "params_dict had keys that were not part of value.params.  "
              "params_dict keys: {}, value.params processed keys: {}".format(
                  sorted(params_dict.keys()), sorted(processed_params)
              )
          )
  
      return Params(type_=value.type_, params=new_params)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import inspect
  from typing import Any, Mapping, Text, Type
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.distributions import tanh_bijector_stable
  from tf_agents.specs import tensor_spec
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 5 for calling a global function with example usage: merge_to_parameters_from_dict(value, params_dict) and returns: Params, merge_to_parameters_from_dict, unknown