code: def train(self, mode=True):
      super().train(mode)
      if mode and self.attention_bias_cache:
          self.attention_bias_cache = {}
  
imports: import itertools
  from collections import OrderedDict
  from typing import Dict
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import SqueezeExcite, SelectAdaptivePool2d, trunc_normal_, _assert
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling the CascadedGroupAttention obj's underlying member method code with example usage: obj.train(mode) without return types