code: def __init__(self, a, b, bias=True, std=0.02):
      super().__init__()
      self.add_sublayer("bn", nn.BatchNorm1D(a))
      l = nn.Linear(a, b, bias_attr=bias)
      trunc_normal_(l.weight)
      if bias:
          zeros_(l.bias)
      self.add_sublayer("l", l)
  
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 1 to create a new BN_Linear object from inherited base classes: nn with example: obj = BN_Linear(a, b, bias, std)