code: def _patch_merging_pad(x: torch.Tensor) -> torch.Tensor:
      H, W, _ = x.shape[-3:]
      x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
      x0 = x[..., 0::2, 0::2, :]  # ... H/2 W/2 C
      x1 = x[..., 1::2, 0::2, :]  # ... H/2 W/2 C
      x2 = x[..., 0::2, 1::2, :]  # ... H/2 W/2 C
      x3 = x[..., 1::2, 1::2, :]  # ... H/2 W/2 C
      x = torch.cat([x0, x1, x2, x3], -1)  # ... H/2 W/2 4*C
      return x
  
imports: import math
  from functools import partial
  from typing import Any, Callable, List, Optional
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
  from ..ops.misc import MLP, Permute
  from ..ops.stochastic_depth import StochasticDepth
  from ..transforms._presets import ImageClassification, InterpolationMode
  from ..utils import _log_api_usage_once
  from ._api import register_model, Weights, WeightsEnum
  from ._meta import _IMAGENET_CATEGORIES
  from ._utils import _ovewrite_named_param, handle_legacy_interface
label_desc: Use image node_id 1 for calling a global function with example usage: _patch_merging_pad(x) and returns: x