class_docstr: An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
  models.
class_docstr_tok: ["An","abstract","class","to","handle","weights","initialization","and","a","simple","interface","for","downloading","and","loading","pretrained","models","."]
docstr: Initialize the weights
docstr_tok: ["Initialize","the","weights"]
code: def _init_weights(self, module):
      """Initialize the weights"""
      if isinstance(module, nn.Linear):
          nn.init.trunc_normal_(module.weight, std=0.02)
          if isinstance(module, nn.Linear) and module.bias is not None:
              nn.init.constant_(module.bias, 0)
      elif isinstance(module, nn.LayerNorm):
          nn.init.constant_(module.bias, 0)
          nn.init.constant_(module.weight, 1.0)
      elif isinstance(module, nn.Conv2d):
          fan_out = (
              module.kernel_size[0]
              * module.kernel_size[1]
              * module.out_channels
          )
          fan_out //= module.groups
          module.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
          if module.bias is not None:
              module.bias.data.zero_()
  
imports: import math
  from collections import OrderedDict
  from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_van import VanConfig
label_desc: Use image node_id 1 for calling the VanPreTrainedModel obj's underlying member method code with example usage: obj._init_weights(module) without return types