class_docstr: Pretty standard MLP.
  :param in_dim: integer input dimensionality
  :param mlp_dim: integer mlp dimensionality. Defaults to in_dim.
  :param out_dim: integer output dimensionality. Defaults to in_dim.
  :param act_layer: activation function. Defaults to nn.GELU
  :param drop: dropout probability. Defaults to 0
class_docstr_tok: ["Pretty","standard","MLP",".",":","param","in_dim",":","integer","input","dimensionality",":","param","mlp_dim",":","integer","mlp","dimensionality",".","Defaults","to","in_dim",".",":","param","out_dim",":","integer","output","dimensionality",".","Defaults","to","in_dim",".",":","param","act_layer",":","activation","function",".","Defaults","to","nn.GELU",":","param","drop",":","dropout","probability",".","Defaults","to","0"]
code: def forward(self, x):
      x = self.linear_1(x)
      x = self.act(x)
      x = self.dropout_1(x)
      x = self.linear_2(x)
      x = self.dropout_2(x)
      return x
  
imports: import copy
  import logging
  import math
  import torch
  import torch.nn
  from vissl.models.model_helpers import DropPath, to_2tuple, trunc_normal_
  from vissl.models.trunks import register_model_trunk
  from vissl.utils.hydra_config import AttrDict
label_desc: Use image node_id 3 for calling the Mlp obj's underlying member method code with example usage: obj.forward(x) and returns: x