code: def load_batch(self, data_iter):
      super().load_batch(data_iter)
      # overwrite microbatch_offset, since model chunks load the same microbatch, and should tract the offset
      self.microbatch_offset = [0 for _ in range(self.num_model_chunks)]
  
imports: import inspect
  from typing import Callable, List, Tuple, Union
  import torch.cuda
  import colossalai.legacy.communication
  from colossalai.legacy.amp.naive_amp import NaiveAMPModel
  from colossalai.legacy.context.parallel_mode import ParallelMode
  from colossalai.legacy.core import global_context
  from colossalai.legacy.utils import switch_virtual_pipeline_parallel_rank
  from colossalai.logging import get_dist_logger
  from colossalai.utils.device import get_current_device
  from ._base_schedule import BaseSchedule
label_desc: Use image node_id 3 for calling the InterleavedPipelineSchedule obj's underlying member method code with example usage: obj.load_batch(data_iter) without return types