class_docstr: This module can be used to attach combination of {Linear, BatchNorm, Relu, Dropout}
  layers and they are fully configurable from the config file. The module also supports
  stacking multiple MLPs.
  
  Examples:
      Linear
      Linear -> BN
      Linear -> ReLU
      Linear -> Dropout
      Linear -> BN -> ReLU -> Dropout
      Linear -> ReLU -> Dropout
      Linear -> ReLU -> Linear -> ReLU -> ...
      Linear -> Linear -> ...
      ...
  
  Accepts a 2D input tensor. Also accepts 4D input tensor of shape `N x C x 1 x 1`.
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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.py
imports: from typing import List
  import torch
  import torch.nn
  from vissl.config import AttrDict
  from vissl.models.heads import register_model_head
  from vissl.utils.fsdp_utils import fsdp_auto_wrap_bn, fsdp_wrapper
label_desc: Use image node_id 3 for calling the MLP obj's underlying member method code with example usage: obj.scale_weights(model_config) without return types