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","."]
code: def add_arguments(
      cls, parser: argparse.ArgumentParser
  ) -> argparse.ArgumentParser:
      if cls != ByteFormer:
          # Don't re-register arguments in subclasses that don't override `add_arguments()`.
          return parser
      group = parser.add_argument_group(title=cls.__name__)
      group.add_argument(
          "--model.classification.byteformer.dropout",
          type=float,
          default=0.0,
          help="Dropout in Byteformer layers. Defaults to 0.0.",
      )
      group.add_argument(
          "--model.classification.byteformer.stochastic-dropout",
          type=float,
          default=0.0,
          help="Probability of applying stochastic dropout to "
          "TransformerEncoder submodules. Defaults to 0.0.",
      )
      group.add_argument(
          "--model.classification.byteformer.norm-layer",
          type=str,
          default="layer_norm",
          help="Normalization layer in Byteformer. Defaults to LayerNorm.",
          choices=list(normalization.NORM_LAYER_REGISTRY.keys()),
      )
      group.add_argument(
          "--model.classification.byteformer.sinusoidal-pos-emb",
          action="store_true",
          default=False,
          help="Use sinusoidal instead of learnable positional encoding. Defaults to False.",
      )
      group.add_argument(
          "--model.classification.byteformer.use-pytorch-mha",
          action="store_true",
          default=False,
          help="Use PyTorch's native multi-head attention. Defaults to False.",
      )
      group.add_argument(
          "--model.classification.byteformer.mode",
          type=str,
          default="tiny",
          help="Byteformer mode, which determines the model size. Defaults to tiny.",
          choices=("tiny", "small", "base", "huge"),
      )
      group.add_argument(
          "--model.classification.byteformer.vocab-size",
          type=int,
          help="The vocab size of the token embedding. Defaults to 257,"
          "corresponding to the number of unique bytes (256) plus 1 "
          "more for the mask token.",
          default=257,
      )
      group.add_argument(
          "--model.classification.byteformer.max-num-tokens",
          type=int,
          help="The maximum number of tokens that can be input to the network. Defaults to 10000.",
          default=10000,
      )
      group.add_argument(
          "--model.classification.byteformer.conv-kernel-size",
          type=int,
          default=16,
          help="The size of the kernel of the initial downsampling conv1d. Defaults to 16.",
      )
      group.add_argument(
          "--model.classification.byteformer.window-sizes",
          type=int,
          nargs="*",
          default=[128],
          help="A list of window sizes used in shifted window attention. If the "
          "list is length 1, the same window size is used for all windows. "
          "Defaults to 128 for all windows.",
      )
      group.add_argument(
          "--model.classification.byteformer.window-shifts",
          type=int,
          nargs="*",
          default=[0, 64] * 6,
          help="A list of shifts used in shifted window attention. Defaults to values that alternate between 0 and 64.",
      )
      default_downsampling = (
          [True, True] + ([False, True] * 4) + [False, False]
      )
      group.add_argument(
          "--model.classification.byteformer.downsample",
          type=bool,
          nargs="*",
          default=default_downsampling,
          help="A list of boolean values, where the i'th element specifies "
          "whether to downsample after the transformer block with index i. "
          f"Defaults to {default_downsampling}.",
      )
      group.add_argument(
          "--model.classification.byteformer.padding-index",
          default=-1,
          type=int,
          help="The index used for padding tokens. Defaults to -1.",
      )
      group.add_argument(
          "--model.classification.byteformer.dummy-input-token-length",
          default=48564,
          type=int,
          help="The token length to use for dummy inputs. Defaults to 48564, "
          "corresponding to the average length of 224x224 JPEG images from "
          "ImageNet.",
      )
      return parser
  
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 2 for calling the ByteFormer obj's underlying member method code with example usage: obj.add_arguments(cls, parser) and returns: parser, parser