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 __init__(self, config: SwiftFormerConfig, in_features: int):
      super().__init__()
      hidden_features = int(in_features * config.mlp_ratio)
      self.norm1 = nn.BatchNorm2d(
          in_features, eps=config.batch_norm_eps
      )
      self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
      act_layer = ACT2CLS[config.hidden_act]
      self.act = act_layer()
      self.fc2 = nn.Conv2d(hidden_features, in_features, 1)
      self.drop = nn.Dropout(p=0.0)
  
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 1 to create a new SwiftFormerMlp object from inherited base classes: nn with example: obj = SwiftFormerMlp(config, in_features)