code: def unfold_tokens(t: Tensor, kernel_size: int) -> Tensor:
      """
      Group tokens from tensor @t using torch.Tensor.unfold, using the given
      kernel size. This amounts to windowing @t using overlapping windows
      of size @kernel_size, with overlap of @kernel_size // 2.
  
      Args:
          t: A tensor of shape [batch_size, sequence_length, num_channels].
          kernel_size: The kernel size.
  
      Returns:
          A tensor of shape [batch_size * (sequence_length - kernel_size)
          // (kernel_size // 2) + 1, kernel_size, num_channels].
      """
      t = t.unfold(dimension=1, size=kernel_size, step=kernel_size // 2)
      B, L, C, _ = t.shape
      t = t.reshape(B * L, C, kernel_size)
      t = t.transpose(1, 2)
      return t
  
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 1 for calling a global function with example usage: unfold_tokens(t, kernel_size) and returns: t