code: def _fill_replay_buffer(self):
      # Generate N frames: the value of pixels is the frame index.
      # The observations will be generated by stacking K frames out of those N,
      # generating some redundancies between the observations.
      single_frames = []
      frame_count = 100
      for k in range(frame_count):
          single_frames.append(
              np.full(self._single_shape, k, dtype=np.int32)
          )
  
      # Add stack of frames to the replay buffer.
      time_steps = []
      for k in range(len(single_frames) - self._stack_count + 1):
          observation = np.concatenate(
              single_frames[k : k + self._stack_count], axis=-1
          )
          time_steps.append(ts.transition(observation, reward=0.0))
  
      self._transition_count = len(time_steps) - 1
      dummy_action = policy_step.PolicyStep(np.int32(0))
      for k in range(self._transition_count):
          self._replay_buffer.add_batch(
              nest_utils.batch_nested_array(
                  trajectory.from_transition(
                      time_steps[k], dummy_action, time_steps[k + 1]
                  )
              )
          )
  
imports: from __future__ import division
  from __future__ import unicode_literals
  import os
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.replay_buffers import py_hashed_replay_buffer
  from tf_agents.replay_buffers import py_uniform_replay_buffer
  from tf_agents.specs import array_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 2 for calling the PyUniformReplayBufferTest obj's underlying member method code with example usage: obj._fill_replay_buffer() without return types