code: def get_mock_batch(
      action_shape: Tuple[int, ...],
      observation_shape: Tuple[int, ...] = OBSERVATION_SHAPE,
      batch_size: int = 2,
      stack_size: int = 1,
  ):
      action_dim = functools.reduce(operator.mul, action_shape, 1)
      mock_observation = np.full(
          observation_shape + (stack_size,), 1.0, dtype=np.float32
      )
      mock_action = np.arange(action_dim, dtype=np.float32).reshape(
          action_shape
      )
      mock_action = mock_action / np.max(
          mock_action
      )  # Squish to action limits
      mock_reward = np.asarray(1.0)
      mock_terminal = np.asarray(0.0)
      mock_indices = np.asarray(0)
      mock_sampling_prob = np.asarray(1.0)
  
      return (
          np.stack(
              [mock_observation for _ in range(batch_size)], axis=0
          ),
          np.stack([mock_action for _ in range(batch_size)], axis=0),
          np.stack([mock_reward for _ in range(batch_size)], axis=0),
          np.stack(
              [mock_observation for _ in range(batch_size)], axis=0
          ),
          np.stack([mock_action for _ in range(batch_size)], axis=0),
          np.stack([mock_reward for _ in range(batch_size)], axis=0),
          np.stack([mock_terminal for _ in range(batch_size)], axis=0),
          np.stack([mock_indices for _ in range(batch_size)], axis=0),
          np.stack(
              [mock_sampling_prob for _ in range(batch_size)], axis=0
          ),
      )
  
imports: import copy
  import functools
  import operator
  import random
  from typing import Any, Tuple, Union
  from unittest import mock
  from absl.testing import absltest
  from absl.testing import parameterized
  from dopamine.jax.agents.sac import sac_agent
  import flax
  import gin
  import jax
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: get_mock_batch(action_shape, observation_shape, batch_size, stack_size) and returns: np,  np,  np,  np,  np,  np,  np,  np,  np