class_docstr: Vision Transformer with support for patch input
      
class_docstr_tok: ["Vision","Transformer","with","support","for","patch","input"]
code: def _init_weights(self, m):
      if isinstance(m, nn.Linear):
          trunc_normal_(m.weight)
          if isinstance(m, nn.Linear) and m.bias is not None:
              zeros_(m.bias)
      elif isinstance(m, nn.LayerNorm):
          zeros_(m.bias)
          ones_(m.weight)
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn import Layer
  from paddle.nn import Linear
  from paddle.nn.initializer import Constant
  from paddle.nn.initializer import Normal
  from paddle.nn.initializer import TruncatedNormal
label_desc: Use image node_id 2 for calling the VisionTransformer obj's underlying member method code with example usage: obj._init_weights(m) without return types