class_docstr: A variant of MLP in which we introduce skip connections between
  successive layers in case the dimensions allow it
class_docstr_tok: ["A","variant","of","MLP","in","which","we","introduce","skip","connections","between","successive","layers","in","case","the","dimensions","allow","it"]
code: def scale_weights(self, model_config):
      params_multiplier = model_config.HEAD.PARAMS_MULTIPLIER
      for m in self.modules():
          if isinstance(m, nn.Linear):
              m.weight.data *= params_multiplier
              if m.bias is not None:
                  m.bias.data *= params_multiplier
  
file_path: vissl/vissl/models/heads/mlp_res.py
imports: from typing import List
  import torch
  import torch.nn
  from vissl.config import AttrDict
  from vissl.models.heads import register_model_head
label_desc: Use image node_id 3 for calling the MLPResidual obj's underlying member method code with example usage: obj.scale_weights(model_config) without return types