code: def get_parameters(value: Any) -> Params:
      """Creates a recursive `Params` object from `value`.
  
      The `Params` object can be converted back to an object of type `type(value)`
      with `make_from_parameters`.  For more details, see the docstring of
      `Params`.
  
      Args:
        value: Typically a user provides `tfp.Distribution`, `tfp.Bijector`, or
          `tf.linalg.LinearOperator`, but this can be any Python object.
  
      Returns:
        An instance of `Params`.
  
      Raises:
        TypeError: If `value.parameters` exists, is not `None`, but but is also not
           a `Mapping` (e.g. a `dict`).
      """
      parameters = getattr(value, "parameters", None)
      if not isinstance(parameters, Mapping):
          raise TypeError(
              "value.parameters is not available or is not a dict; "
              "value: {}; parameters: {}".format(value, parameters)
          )
      type_ = type(value)
      params = {}
  
      def process_parameter(p):
          if getattr(p, "parameters", None) is not None:
              return get_parameters(p)
          else:
              return p
  
      if getattr(value, "parameters"):
          default_values = inspect.signature(
              type_.__init__
          ).parameters.items()
          default_values = {
              k: v.default
              for (k, v) in default_values
              if v.default is not inspect.Parameter.empty
          }
          params = {
              k: tf.nest.map_structure(process_parameter, v)
              for k, v in value.parameters.items()
              if v is not default_values.get(k, None)
          }
          return Params(type(value), params)
  
      return value
  
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 2 for calling a global function with example usage: get_parameters(value) and returns: value, Params, get_parameters, p