class_docstr: Perform linear projection or Transformer-based decoder (optionally MultiScale)
  for mask prediction models.
  Args:
      blocks (MultiScaleBlock): the encoder blocks to provide input dimensions of the head.
      num_classes (int): the dimension of the prediction target (eg. HOG or pixels).
      feat_sz (list): the spatiotemporal sizes of the input features.
class_docstr_tok: ["Perform","linear","projection","or","Transformer-based","decoder","(","optionally","MultiScale",")","for","mask","prediction","models",".","Args",":","blocks","(","MultiScaleBlock",")",":","the","encoder","blocks","to","provide","input","dimensions","of","the","head",".","num_classes","(","int",")",":","the","dimension","of","the","prediction","target","(","eg",".","HOG","or","pixels",")",".","feat_sz","(","list",")",":","the","spatiotemporal","sizes","of","the","input","features","."]
code: def _init_weights(self, m):
      if isinstance(m, nn.Linear):
          nn.init.trunc_normal_(m.weight, std=0.02)
          # FYI: MAE uses xavier_uniform following official JAX ViT:
          # torch.nn.init.xavier_uniform_(m.weight)
          if isinstance(m, nn.Linear) and m.bias is not None:
              nn.init.constant_(m.bias, 0)
      elif isinstance(m, nn.LayerNorm):
          nn.init.constant_(m.bias, 0)
          nn.init.constant_(m.weight, 1.0)
  
imports: from functools import partial
  import torch
  import torch.nn
  from detectron2.layers import ROIAlign
  import slowfast.utils.logging
  from slowfast.models.attention import MultiScaleBlock
  from slowfast.models.batchnorm_helper import NaiveSyncBatchNorm1d
  from slowfast.models.nonlocal_helper import Nonlocal
label_desc: Use image node_id 2 for calling the MSSeparateHead obj's underlying member method code with example usage: obj._init_weights(m) without return types