code: def generate_invalid_episodes() -> (
      Dict[str, Tuple[tf.data.Dataset, str]]
  ):
      """Get test data for invalid RLDS datasets to be used across differrent tests.
  
      Returns:
        A dict representing all invalid cases as keys and tuples of invalid input
        RLDS and expected error messages as values.
      """
      return {
          "incorrect_ending": (
              tf.data.Dataset.from_tensor_slices(
                  {
                      rlds_types.STEPS: [
                          tf.data.Dataset.from_tensor_slices(
                              {
                                  rlds_types.OBSERVATION: _OBSERVATIONS,
                                  rlds_types.ACTION: _ACTIONS,
                                  rlds_types.REWARD: _REWARDS,
                                  rlds_types.DISCOUNT: _DISCOUNTS,
                                  rlds_types.IS_TERMINAL: [
                                      False,
                                      False,
                                      False,
                                  ],
                                  rlds_types.IS_LAST: [
                                      False,
                                      False,
                                      False,
                                  ],
                                  rlds_types.IS_FIRST: [
                                      True,
                                      False,
                                      True,
                                  ],
                              }
                          )
                      ],
                  }
              ),
              "Mid step should not be followed by a first step.",
          ),
          "incorrect_termination": (
              tf.data.Dataset.from_tensor_slices(
                  {
                      rlds_types.STEPS: [
                          tf.data.Dataset.from_tensor_slices(
                              {
                                  rlds_types.OBSERVATION: _OBSERVATIONS,
                                  rlds_types.ACTION: _ACTIONS,
                                  rlds_types.REWARD: _REWARDS,
                                  rlds_types.DISCOUNT: _DISCOUNTS,
                                  rlds_types.IS_TERMINAL: [
                                      False,
                                      False,
                                      True,
                                  ],
                                  rlds_types.IS_LAST: [
                                      False,
                                      False,
                                      False,
                                  ],
                                  rlds_types.IS_FIRST: [
                                      True,
                                      False,
                                      False,
                                  ],
                              }
                          )
                      ],
                  }
              ),
              "Terminal step must be the last step of an episode.",
          ),
          "incorrect_beginning": (
              tf.data.Dataset.from_tensor_slices(
                  {
                      rlds_types.STEPS: [
                          tf.data.Dataset.from_tensor_slices(
                              {
                                  rlds_types.OBSERVATION: _OBSERVATIONS,
                                  rlds_types.ACTION: _ACTIONS,
                                  rlds_types.REWARD: _REWARDS,
                                  rlds_types.DISCOUNT: _DISCOUNTS,
                                  rlds_types.IS_TERMINAL: [
                                      False,
                                      True,
                                      False,
                                  ],
                                  rlds_types.IS_LAST: [
                                      False,
                                      True,
                                      False,
                                  ],
                                  rlds_types.IS_FIRST: [
                                      True,
                                      False,
                                      False,
                                  ],
                              }
                          )
                      ],
                  }
              ),
              "Last step of an episode must be followed by a first step.",
          ),
          "different_spec_episode": (
              tf.data.Dataset.from_tensor_slices(
                  {
                      rlds_types.STEPS: [
                          tf.data.Dataset.from_tensor_slices(
                              {
                                  rlds_types.OBSERVATION: [
                                      [1.0],
                                      [3.0],
                                      [5.0],
                                  ],
                                  rlds_types.ACTION: _ACTIONS,
                                  rlds_types.REWARD: _REWARDS,
                                  rlds_types.DISCOUNT: _DISCOUNTS,
                                  rlds_types.IS_TERMINAL: [
                                      False,
                                      True,
                                      False,
                                  ],
                                  rlds_types.IS_LAST: [
                                      False,
                                      True,
                                      False,
                                  ],
                                  rlds_types.IS_FIRST: [
                                      True,
                                      False,
                                      False,
                                  ],
                              }
                          )
                      ],
                  }
              ),
              "Replay buffer table signature spec should match RLDS data spec.",
          ),
          "no_step_episode": (
              tf.data.Dataset.from_tensors({"random": True}),
              f"No dataset representing RLDS {rlds_types.STEPS} exist in the data.",
          ),
          "incorrect_step_spec": (
              tf.data.Dataset.from_tensors(
                  {
                      rlds_types.STEPS: tf.data.Dataset.from_tensors(
                          {
                              "random1": [True, True],
                              "random2": [False, False],
                          }
                      )
                  }
              ),
              (
                  "Invalid RLDS step spec. Features expected are"
                  f" {rlds_to_reverb.get_rlds_step_features()}, but found"
                  " ['random1', 'random2']"
              ),
          ),
      }
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Dict, List, Tuple
  from absl.testing import parameterized
  import reverb
  from rlds import rlds_types
  import tensorflow
  from tf_agents.replay_buffers import reverb_replay_buffer
  from tf_agents.replay_buffers import reverb_utils
  from tf_agents.replay_buffers import rlds_to_reverb
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import test_utils
label_desc: Use image node_id 2 for calling a global function with example usage: generate_invalid_episodes() and returns: dict