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: from paddle import ParamAttr
  from paddle.nn.initializer import KaimingNormal
  import numpy
  import paddle
  import paddle.nn
  from paddle.nn.initializer import TruncatedNormal, Constant, Normal
label_desc: Use image node_id 2 for calling the SVTRNet obj's underlying member method code with example usage: obj._init_weights(m) without return types