code: def train(self, mode=True):
      if mode:
          super().train()
      else:
          super().eval()
      if mode and hasattr(self, "ab"):
          del self.ab
      else:
          self.ab = cal_attention_biases(
              self.attention_biases, self.attention_bias_idxs
          )
  
imports: import itertools
  import math
  import warnings
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import Constant
  from paddle.nn.initializer import TruncatedNormal
  from paddle.regularizer import L2Decay
  from .vision_transformer import Identity
  from .vision_transformer import ones_
  from .vision_transformer import trunc_normal_
  from .vision_transformer import zeros_
label_desc: Use image node_id 2 for calling the Attention obj's underlying member method code with example usage: obj.train(mode) without return types