code: def testIsTransitionValid(self):
      memory = circular_replay_buffer.ReplayBuffer(
          stack_size=STACK_SIZE, replay_capacity=10, batch_size=2
      )
  
      memory.add(
          observation=np.full(OBSERVATION_SHAPE, 0, dtype=OBS_DTYPE),
          action=0,
          reward=0,
          terminal=0,
      )
      memory.add(
          observation=np.full(OBSERVATION_SHAPE, 0, dtype=OBS_DTYPE),
          action=0,
          reward=0,
          terminal=0,
      )
      memory.add(
          observation=np.full(OBSERVATION_SHAPE, 0, dtype=OBS_DTYPE),
          action=0,
          reward=0,
          terminal=1,
      )
  
      # These valids account for the automatically applied padding (3 blanks each
      # episode.
      # correct_valids = [0, 0, 0, 1, 1, 0, 0, 0, 0, 0]
      # The above comment is for the original Dopamine buffer, which doesn't
      # account for terminal frames within the update_horizon frames before
      # the cursor. In this case, the frame right before the cursor
      # is terminal, so even though it is within [c-update_horizon, c],
      # it should still be valid for sampling, as next state doesn't matter.
      correct_valids = [0, 0, 0, 1, 1, 1, 0, 0, 0, 0]
      # The cursor is:                    ^\
      for i in range(10):
          self.assertEqual(
              correct_valids[i],
              memory.is_valid_transition(i),
              "Index %i should be %s" % (i, bool(correct_valids[i])),
          )
  
imports: import tempfile
  import unittest
  import numpy
  import numpy.testing
  import torch
  from reagent.replay_memory import circular_replay_buffer
label_desc: Use image node_id 11 for calling the ReplayBufferTest obj's underlying member method code with example usage: obj.testIsTransitionValid() without return types