code: def forward(self, x: Tensor):
      B, C, H, W = x.shape
      x = self.norm(x)
      x = F.pad(x, (0, (2 - W % 2) % 2))
      x = F.pad(x, (0, 0, 0, (2 - H % 2) % 2))
      x = self.conv(x)
      return x
  
imports: from functools import partial
  from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import DropPath, to_2tuple, trunc_normal_, Mlp, LayerNorm2d, get_norm_layer, use_fused_attn
  from timm.layers import NormMlpClassifierHead, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._manipulate import checkpoint_seq
  from ._registry import generate_default_cfgs, register_model
label_desc: Use image node_id 2 for calling the Downsample obj's underlying member method code with example usage: obj.forward(x) and returns: x