class_docstr: Abstract class to represent a non-stationary environment dynamics.
  
  This class is used with the NonStationaryStochasticEnvironment class below to
  obtain a non-stationary environment.
  To define a dynamics, derive from this class and define the abstract methods
  and properties below.
  To work correctly with graph and eager mode, Tensorflow variables must be
  defined in the constructor of this class. When used within a
  `BanditTFEnvironment` autodeps in reset and step functions will handle
  automatically the operation order.
class_docstr_tok: ["Abstract","class","to","represent","a","non-stationary","environment","dynamics",".","This","class","is","used","with","the","NonStationaryStochasticEnvironment","class","below","to","obtain","a","non-stationary","environment",".","To","define","a","dynamics",",","derive","from","this","class","and","define","the","abstract","methods","and","properties","below",".","To","work","correctly","with","graph","and","eager","mode",",","Tensorflow","variables","must","be","defined","in","the","constructor","of","this","class",".","When","used","within","a","`","BanditTFEnvironment","`","autodeps","in","reset","and","step","functions","will","handle","automatically","the","operation","order","."]
docstr: Returns an observation batch for the given time.
  
  Args:
    env_time: The scalar int64 tensor of the environment time step. This is
      incremented by the environment after the reward is computed.
  
  Returns:
    The observation batch with spec according to `observation_spec.`
docstr_tok: ["Returns","an","observation","batch","for","the","given","time",".","Args",":","env_time",":","The","scalar","int64","tensor","of","the","environment","time","step",".","This","is","incremented","by","the","environment","after","the","reward","is","computed",".","Returns",":","The","observation","batch","with","spec","according","to","`","observation_spec.","`"]
code: def observation(self, env_time: types.Int) -> types.NestedTensor:
      """Returns an observation batch for the given time.
  
      Args:
        env_time: The scalar int64 tensor of the environment time step. This is
          incremented by the environment after the reward is computed.
  
      Returns:
        The observation batch with spec according to `observation_spec.`
      """
      pass
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import gin
  import tensorflow
  from tf_agents.bandits.environments import bandit_tf_environment
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tf_agents.utils import common
label_desc: Use image node_id 4 for calling the EnvironmentDynamics obj's underlying member method code with example usage: obj.observation(env_time) without return types