class_docstr: This class defines the `ByteFormer <https://arxiv.org/pdf/2306.00238.pdf>`_
  architecture.
class_docstr_tok: ["This","class","defines","the","`","ByteFormer","<","https",":","\/\/arxiv.org\/pdf\/2306.00238.pdf",">","`","_","architecture","."]
docstr: Execute the forward pass of the network's transformer backbone.
  
  Args:
      x: The input embeddings as a [batch_size, sequence_length, embed_dim] tensor.
      key_padding_mask: The mask tensor of shape [batch_size, sequence_length].
  
  Returns:
      The outputs of the backbone as a tuple. The first element is the feature
      tensor, and the second element is the updated key_padding_mask.
docstr_tok: ["Execute","the","forward","pass","of","the","network","'s","transformer","backbone",".","Args",":","x",":","The","input","embeddings","as","a","[","batch_size",",","sequence_length",",","embed_dim","]","tensor",".","key_padding_mask",":","The","mask","tensor","of","shape","[","batch_size",",","sequence_length","]",".","Returns",":","The","outputs","of","the","backbone","as","a","tuple",".","The","first","element","is","the","feature","tensor",",","and","the","second","element","is","the","updated","key_padding_mask","."]
code: def backbone_forward(
      self, x: Tensor, key_padding_mask: Tensor
  ) -> Tuple[Tensor, Tensor]:
      """
      Execute the forward pass of the network's transformer backbone.
  
      Args:
          x: The input embeddings as a [batch_size, sequence_length, embed_dim] tensor.
          key_padding_mask: The mask tensor of shape [batch_size, sequence_length].
  
      Returns:
          The outputs of the backbone as a tuple. The first element is the feature
          tensor, and the second element is the updated key_padding_mask.
      """
      B, S, _ = x.shape
      assert key_padding_mask.shape == (B, S)
  
      for layer_idx, elem in enumerate(self.transformer):
          x = elem(x, key_padding_mask=key_padding_mask)
          if self.get_downsampler(layer_idx) is not None:
              x, key_padding_mask = self.get_downsampler(layer_idx)(
                  x, key_padding_mask
              )
      x = self.post_transformer_norm(x)
      return x, key_padding_mask
  
imports: import argparse
  import math
  from typing import Dict, Optional, Tuple, Union
  import numpy
  import torch
  from torch import Tensor, nn
  from torch.nn import init
  from cvnets.layers import LinearLayer, embedding, get_normalization_layer, normalization, positional_embedding, token_merging
  from cvnets.models import MODEL_REGISTRY, BaseAnyNNModel
  from cvnets.models.classification.config.byteformer import get_configuration
  from cvnets.modules import WindowedTransformerEncoder
label_desc: Use image node_id 6 for calling the ByteFormer obj's underlying member method code with example usage: obj.backbone_forward(x, key_padding_mask) and returns: x,  key_padding_mask