class_docstr: Transformer MLP block, fc, gelu, fc.
class_docstr_tok: ["Transformer","MLP","block",",","fc",",","gelu",",","fc","."]
code: def __init__(self, w_in, mlp_d):
      super().__init__()
      self.linear_1 = linear(w_in, mlp_d, bias=True)
      self.af = activation("gelu")
      self.linear_2 = linear(mlp_d, w_in, bias=True)
  
imports: import math
  import numpy
  import torch
  from pycls.core.config import cfg
  from pycls.models.blocks import activation, conv2d, conv2d_cx, layernorm, layernorm_cx, linear, linear_cx, MultiheadAttention, norm2d, norm2d_cx, patchify2d, patchify2d_cx
  from torch.nn import init, Module, Parameter
label_desc: Use image node_id 1 to create a new MLPBlock object from inherited base classes: Module with example: obj = MLPBlock(w_in, mlp_d)