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: Get the name of the downsampling layer with index @idx.
  
  Args:
      idx: The index of the downsampling layer.
  
  Returns:
      A string representing the name of the donwsampling layer.
docstr_tok: ["Get","the","name","of","the","downsampling","layer","with","index","@","idx",".","Args",":","idx",":","The","index","of","the","downsampling","layer",".","Returns",":","A","string","representing","the","name","of","the","donwsampling","layer","."]
code: def get_downsampler_name(self, idx: int) -> str:
      """
      Get the name of the downsampling layer with index @idx.
  
      Args:
          idx: The index of the downsampling layer.
  
      Returns:
          A string representing the name of the donwsampling layer.
      """
      return f"downsample_{idx}"
  
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 7 for calling the ByteFormer obj's underlying member method code with example usage: obj.get_downsampler_name(idx) and returns: str, idx