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)
      elif isinstance(m, nn.Conv2D):
          fan_out = (
              m._kernel_size[0] * m._kernel_size[1] * m._out_channels
          )
          fan_out //= m._groups
          paddle_init.Normal(0, math.sqrt(2.0 / fan_out))(m.weight)
          if m.bias is not None:
              zeros_(m.bias)
  
imports: import math
  from functools import partial
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import paddle.nn.initializer
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
  from paddleseg.models.backbones.transformer_utils import *
label_desc: Use image node_id 3 for calling the MixVisionTransformer obj's underlying member method code with example usage: obj._init_weights(m) without return types