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","."]
code: def _init_weights(self, module):
      if isinstance(module, (nn.Linear, nn.Conv2d)):
          nn.init.trunc_normal_(
              module.weight, std=self.config.initializer_range
          )
          if 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, TimesformerEmbeddings):
          nn.init.trunc_normal_(
              module.cls_token, std=self.config.initializer_range
          )
          nn.init.trunc_normal_(
              module.position_embeddings,
              std=self.config.initializer_range,
          )
          module.patch_embeddings.apply(self._init_weights)
  
imports: import collections
  from typing import Optional, Tuple, Union
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_timesformer import TimesformerConfig
label_desc: Use image node_id 1 for calling the TimesformerPreTrainedModel obj's underlying member method code with example usage: obj._init_weights(module) without return types