class_docstr: Returned with every call to `policy.action()` and `policy.distribution()`.
  
  Attributes:
   action: An action tensor or action distribution for `TFPolicy`, or numpy
     array for `PyPolicy`.
   state: During inference, it will hold the state of the policy to be fed back
     into the next  call to policy.action() or policy.distribution(), e.g. an
     RNN state. During the training, it will hold the state that is input to
     policy.action() or policy.distribution() For stateless policies, this will
     be an empty tuple.
   info: Auxiliary information emitted by the policy, e.g. log probabilities of
     the actions. For policies without info this will be an empty tuple.
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docstr: Exposes as namedtuple._replace.
  
  Usage:
  ```
    new_policy_step = policy_step.replace(action=())
  ```
  
  This returns a new policy step with an empty action.
  
  Args:
    **kwargs: key/value pairs of fields in the policy step.
  
  Returns:
    A new `PolicyStep`.
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code: def replace(self, **kwargs) -> "PolicyStep":
      """Exposes as namedtuple._replace.
  
      Usage:
      ```
        new_policy_step = policy_step.replace(action=())
      ```
  
      This returns a new policy step with an empty action.
  
      Args:
        **kwargs: key/value pairs of fields in the policy step.
  
      Returns:
        A new `PolicyStep`.
      """
      return self._replace(**kwargs)
  
file_path: agents/tf_agents/trajectories/policy_step.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from typing import Any, Mapping, NamedTuple, Optional, Text, Union
  from tf_agents.typing import types
label_desc: Use image node_id 1 for calling the PolicyStep obj's underlying member method code with example usage: obj.replace() and returns: self