class_docstr: Embeddings layer consisting of a single 2D convolutional and batch normalization layer.
  
  Input: tensor of shape `[batch_size, channels, height, width]`
  
  Output: tensor of shape `[batch_size, channels, height/stride, width/stride]`
class_docstr_tok: ["Embeddings","layer","consisting","of","a","single","2D","convolutional","and","batch","normalization","layer",".","Input",":","tensor","of","shape","`","[","batch_size",",","channels",",","height",",","width","]","`","Output",":","tensor","of","shape","`","[","batch_size",",","channels",",","height\/stride",",","width\/stride","]","`"]
code: def forward(self, x):
      x = self.proj(x)
      x = self.norm(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 SwiftFormerEmbeddings obj's underlying member method code with example usage: obj.forward(x) and returns: x