class_docstr: MLP layer with 1*1 convolutions.
  
  Input: tensor of shape `[batch_size, channels, height, width]`
  
  Output: tensor of shape `[batch_size, channels, height, width]`
class_docstr_tok: ["MLP","layer","with","1","*","1","convolutions",".","Input",":","tensor","of","shape","`","[","batch_size",",","channels",",","height",",","width","]","`","Output",":","tensor","of","shape","`","[","batch_size",",","channels",",","height",",","width","]","`"]
code: def forward(self, x):
      x = self.norm1(x)
      x = self.fc1(x)
      x = self.act(x)
      x = self.drop(x)
      x = self.fc2(x)
      x = self.drop(x)
      return x
  
imports: import collections.abc
  from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2CLS
  from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_swiftformer import SwiftFormerConfig
label_desc: Use image node_id 2 for calling the SwiftFormerMlp obj's underlying member method code with example usage: obj.forward(x) and returns: x