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 a dummy input and label that could be passed to the model.
  
  Args:
      batch_size: The batch size to use for the generated inputs.
  
  Returns:
      A dict with
          {
              "samples": tensor of shape [batch_size, sequence_length],
              "targets": tensor of shape [batch_size],
          }
docstr_tok: ["Get","a","dummy","input","and","label","that","could","be","passed","to","the","model",".","Args",":","batch_size",":","The","batch","size","to","use","for","the","generated","inputs",".","Returns",":","A","dict","with","{","``","samples","''",":","tensor","of","shape","[","batch_size",",","sequence_length","]",",","``","targets","''",":","tensor","of","shape","[","batch_size","]",",","}"]
code: def dummy_input_and_label(self, batch_size: int) -> Dict:
      """
      Get a dummy input and label that could be passed to the model.
  
      Args:
          batch_size: The batch size to use for the generated inputs.
  
      Returns:
          A dict with
              {
                  "samples": tensor of shape [batch_size, sequence_length],
                  "targets": tensor of shape [batch_size],
              }
      """
      n_labels = 10
      max_value = 257
  
      samples = torch.randint(
          0, max_value, [batch_size, self.dummy_input_token_length]
      )
      targets = torch.randint(
          low=0, high=n_labels, size=(batch_size,)
      ).long()
      return {"samples": samples, "targets": targets}
  
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 3 for calling the ByteFormer obj's underlying member method code with example usage: obj.dummy_input_and_label(batch_size) and returns: dict