class_docstr: Local Representation module for SwiftFormer that is implemented by 3*3 depth-wise and point-wise convolutions.
  
  Input: tensor of shape `[batch_size, channels, height, width]`
  
  Output: tensor of shape `[batch_size, channels, height, width]`
class_docstr_tok: ["Local","Representation","module","for","SwiftFormer","that","is","implemented","by","3","*","3","depth-wise","and","point-wise","convolutions",".","Input",":","tensor","of","shape","`","[","batch_size",",","channels",",","height",",","width","]","`","Output",":","tensor","of","shape","`","[","batch_size",",","channels",",","height",",","width","]","`"]
code: def forward(self, x):
      input = x
      x = self.depth_wise_conv(x)
      x = self.norm(x)
      x = self.point_wise_conv1(x)
      x = self.act(x)
      x = self.point_wise_conv2(x)
      x = input + self.drop_path(self.layer_scale * 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 SwiftFormerLocalRepresentation obj's underlying member method code with example usage: obj.forward(x) and returns: x