class_docstr: Merge tokens from a [batch_size, sequence_length, num_channels] tensor
  using a linear projection.
  
  This function also updates masks and adds padding as needed to make the
  sequence length divisible by the window size before merging tokens.
  
  Args:
      dim: Number of input channels.
      window: The size of the window to merge into a single token.
code: def __init__(self, dim: int, window: int = 2) -> None:
      super().__init__()
      self.dim = dim
      self.reduction = linear_layer.LinearLayer(
          window * dim, dim, bias=False
      )
      self.norm = layer_norm.LayerNorm(dim)
      self.window = window
  
file_path: ml-cvnets/cvnets/layers/token_merging.py
imports: from typing import Tuple
  import torch
  from torch import nn
  from torch.nn import functional
  from cvnets.layers import linear_layer
  from cvnets.layers.normalization import layer_norm
label_desc: Use image node_id 1 to create a new TokenMerging object from inherited base classes: nn with example: obj = TokenMerging(dim, window)