code: def unwindow_x(
      x_windows: torch.Tensor, B: int, N: int, C: int, window_shift: int
  ):
      """
      Undoes the operation of @window_x_and_attention on the input tensor @x_windows.
  
      Args:
          x_windows: The input tensor to unwindow. Its shape is [batch_size *
                padded_sequence_length // window_size, window_size, embed_dim].
          B: The batch size. Referred to as batch_size in this docstring.
          N: The sequence length of the tensor before windowing. Referred to as
              sequence_length in this docstring.
          C: The number of channels. Referred to as embed_dim in this docstring.
          window_shift: The shift applied to the sequence before the windowing
              originally occurred.
  
      Returns:
          A tensor of shape [batch_size, sequence_length, embed_dim].
      """
      num_windows = x_windows.shape[0] // B
      x = window_partition_reverse(x_windows, B, num_windows, C)
  
      if window_shift > 0:
          x = torch.roll(x, shifts=window_shift, dims=1)
      x = x[:, :N]
  
      return x
  
file_path: ml-cvnets/cvnets/modules/windowed_transformer.py
imports: import argparse
  from typing import Optional, Tuple
  import torch
  from torch import Tensor
  from cvnets.layers import token_merging
  from cvnets.modules import transformer
label_desc: Use image node_id 5 for calling a global function with example usage: unwindow_x(x_windows, B, N, C, window_shift) and returns: x