code: def forward(self, x: Tensor) -> Tensor:
      if x.ndim == 3:
          x = x.transpose(1, 2).unflatten(
              2, self.input_resolution
          )  # (B, H * W, C) -> (B, C, H, W)
      x = self.conv3(self.conv2(self.conv1(x)))
      x = x.flatten(2).transpose(1, 2)  # (B, C, H, W) -> (B, H * W, C)
      return x
  
imports: from __future__ import annotations
  import itertools
  import warnings
  from typing import Any, Optional
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.utils import checkpoint
  from kornia.contrib.models.common import DropPath, LayerNorm2d, window_partition, window_unpartition
  from kornia.core import Module, Tensor
  from kornia.core.check import KORNIA_CHECK
label_desc: Use image node_id 2 for calling the PatchMerging obj's underlying member method code with example usage: obj.forward(x) and returns: x