code: def __init__(self, input_dim, output_dim, patch_size):
      super().__init__()
      self.down = nn.Conv2d(
          input_dim,
          output_dim,
          kernel_size=patch_size,
          stride=patch_size,
      )
  
imports: import math
  from functools import partial
  from itertools import accumulate
  from typing import Tuple
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DEFAULT_CROP_PCT
  from timm.layers import lecun_normal_, DropPath, Mlp, PatchEmbed, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._manipulate import named_apply
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new Downsample2d object from inherited base classes: nn with example: obj = Downsample2d(input_dim, output_dim, patch_size)