code: def split_and_pad(episodes_data, T):
      all_chunks = []
  
      for data in episodes_data:
          num_chunks = int(np.ceil(data.shape[0] / T))
  
          for i in range(num_chunks):
              start_index = i * T
              end_index = start_index + T
  
              # Extract the chunk
              chunk = data[start_index:end_index]
  
              # Pad the chunk if it's shorter than T
              if chunk.shape[0] < T:
                  padding_shape = [(0, T - chunk.shape[0])] + [
                      (0, 0) for _ in range(chunk.ndim - 1)
                  ]
                  chunk = np.pad(
                      chunk, pad_width=padding_shape, mode="constant"
                  )
  
              all_chunks.append(chunk)
  
      # Combine all chunks into a single array
      result = np.concatenate(all_chunks, axis=0)
  
      # Reshape the array to include the time dimension T.
      # The new shape should be (-1, T) + original dimensions (excluding the batch
      # dimension)
      result = result.reshape((-1, T) + result.shape[1:])
  
      return result
  
file_path: ray/rllib/connectors/learner/default_learner_connector.py
imports: from functools import partial
  from typing import Any, List, Optional
  import numpy
  import tree
  from ray.rllib.connectors.connector_v2 import ConnectorV2
  from ray.rllib.core.models.base import STATE_IN, STATE_OUT
  from ray.rllib.core.rl_module.rl_module import RLModule
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.typing import EpisodeType
label_desc: Use image node_id 1 for calling a global function with example usage: split_and_pad(episodes_data, T) and returns: result