code: def __call__(self, data):
      for f in self.transforms_cls:
          try:
              data = f(data)
          except Exception as e:
              stack_info = traceback.format_exc()
              logger.warning(
                  "fail to map batch transform [{}] "
                  "with error: {} and stack:\n{}".format(
                      f, e, str(stack_info)
                  )
              )
              raise e
  
      # remove keys which is not needed by model
      extra_key = ["h", "w", "flipped"]
      for k in extra_key:
          for sample in data:
              if k in sample:
                  sample.pop(k)
  
      # batch data, if user-define batch function needed
      # use user-defined here
      if self.collate_batch:
          batch_data = default_collate_fn(data)
      else:
          batch_data = {}
          for k in data[0].keys():
              tmp_data = []
              for i in range(len(data)):
                  tmp_data.append(data[i][k])
              if (
                  not "gt_" in k
                  and not "is_crowd" in k
                  and not "difficult" in k
              ):
                  tmp_data = np.stack(tmp_data, axis=0)
              batch_data[k] = tmp_data
      return batch_data
  
imports: import copy
  import os
  import traceback
  import six
  import sys
  import numpy
  import paddle
  import paddle.nn.functional
  from copy import deepcopy
  from paddle.io import DataLoader, DistributedBatchSampler
  from .utils import default_collate_fn
  from ppdet.core.workspace import register
  from .None import transform
  from .shm_utils import _get_shared_memory_size_in_M
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 2 for calling the BatchCompose obj's underlying member method code with example usage: obj.__call__(data) and returns: batch_data