code: def __init__(self, transforms, num_classes=80):
      self.transforms = transforms
      self.transforms_cls = []
      for t in self.transforms:
          for k, v in t.items():
              op_cls = getattr(transform, k)
              f = op_cls(**v)
              if hasattr(f, "num_classes"):
                  f.num_classes = num_classes
  
              self.transforms_cls.append(f)
  
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 1 to create a new Compose object from inherited base classes: object with example: obj = Compose(transforms, num_classes)