code: def _init_resnet_weights(
      model: nn.Module, fc_init_std: float = 0.01
  ) -> None:
      """
      Performs ResNet style weight initialization. That is, recursively initialize the
      given model in the following way for each type:
          Conv - Follow the initialization of kaiming_normal:
              https://pytorch.org/docs/stable/_modules/torch/nn/init.html#kaiming_normal_
          BatchNorm - Set weight and bias of last BatchNorm at every residual bottleneck
              to 0.
          Linear - Set weight to 0 mean Gaussian with std deviation fc_init_std and bias
              to 0.
      Args:
          model (nn.Module): Model to be initialized.
          fc_init_std (float): the expected standard deviation for fully-connected layer.
      """
      for m in model.modules():
          if isinstance(m, (nn.Conv2d, nn.Conv3d)):
              """
              Follow the initialization method proposed in:
              {He, Kaiming, et al.
              "Delving deep into rectifiers: Surpassing human-level
              performance on imagenet classification."
              arXiv preprint arXiv:1502.01852 (2015)}
              """
              c2_msra_fill(m)
          elif isinstance(m, nn.modules.batchnorm._NormBase):
              if m.weight is not None:
                  if hasattr(m, "block_final_bn") and m.block_final_bn:
                      m.weight.data.fill_(0.0)
                  else:
                      m.weight.data.fill_(1.0)
              if m.bias is not None:
                  m.bias.data.zero_()
          if isinstance(m, nn.Linear):
              if hasattr(m, "xavier_init") and m.xavier_init:
                  c2_xavier_fill(m)
              else:
                  m.weight.data.normal_(mean=0.0, std=fc_init_std)
              if m.bias is not None:
                  m.bias.data.zero_()
      return model
  
imports: import torch.nn
  from fvcore.nn.weight_init import c2_msra_fill, c2_xavier_fill
  from pytorchvideo.layers import SpatioTemporalClsPositionalEncoding
label_desc: Use image node_id 1 for calling a global function with example usage: _init_resnet_weights(model, fc_init_std) and returns: model