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: Helper function to build a model.
  
  Args:
      opts: Command-line arguments.
  
  Returns:
      An instance of `cvnets.models.BaseAnyNNModel`.
docstr_tok: ["Helper","function","to","build","a","model",".","Args",":","opts",":","Command-line","arguments",".","Returns",":","An","instance","of","`","cvnets.models.BaseAnyNNModel","`","."]
code: def build_model(
      cls, opts: argparse.Namespace, *args, **kwargs
  ) -> BaseAnyNNModel:
      """
      Helper function to build a model.
  
      Args:
          opts: Command-line arguments.
  
      Returns:
          An instance of `cvnets.models.BaseAnyNNModel`.
      """
      model = cls(opts, *args, **kwargs)
  
      if getattr(opts, "model.classification.freeze_batch_norm"):
          cls.freeze_norm_layers(opts=opts, model=model)
      return model
  
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 10 for calling the ByteFormer obj's underlying member method code with example usage: obj.build_model(cls, opts) and returns: model