class_docstr: A tuple that represents a trajectory.
  
  A `Trajectory` represents a sequence of aligned time steps. It captures the
  observation, step_type from current time step with the computed action
  and policy_info. Discount, reward and next_step_type come from the next
  time step.
  
  Attributes:
    step_type: A `StepType`.
    observation: An array (tensor), or a nested dict, list or tuple of arrays
      (tensors) that represents the observation.
    action: An array/a tensor, or a nested dict, list or tuple of actions. This
      represents action generated according to the observation.
    policy_info: An arbitrary nest that contains auxiliary information related
      to the action. Note that this does not include the policy/RNN state which
      was used to generate the action.
    next_step_type: The `StepType` of the next time step.
    reward: An array/a tensor, or a nested dict, list, or tuple of rewards. This
      represents the rewards and/or constraint satisfiability after performing
      the action in an environment.
    discount: A scalar that representing the discount factor to multiply with
      future rewards.
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docstr: Exposes as namedtuple._replace.
  
  Usage:
  ```
    new_trajectory = trajectory.replace(policy_info=())
  ```
  
  This returns a new trajectory with an empty policy_info.
  
  Args:
    **kwargs: key/value pairs of fields in the trajectory.
  
  Returns:
    A new `Trajectory`.
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code: def replace(self, **kwargs) -> "Trajectory":
      """Exposes as namedtuple._replace.
  
      Usage:
      ```
        new_trajectory = trajectory.replace(policy_info=())
      ```
  
      This returns a new trajectory with an empty policy_info.
  
      Args:
        **kwargs: key/value pairs of fields in the trajectory.
  
      Returns:
        A new `Trajectory`.
      """
      return self._replace(**kwargs)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import pprint
  from typing import NamedTuple, Optional
  import numpy
  import tensorflow
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tf_agents.utils import composite
  from tf_agents.utils import nest_utils
  from tf_agents.utils import value_ops
label_desc: Use image node_id 5 for calling the Trajectory obj's underlying member method code with example usage: obj.replace() and returns: self