code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
  ) -> None:
      super().__init__()
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.deconv = nn.ConvTranspose2d(
          in_channels, out_channels, kernel_size=4, stride=2, padding=1
      )
  
imports: from dataclasses import dataclass
  from typing import Optional
  import numpy
  import torch
  import torch.nn
  from ..utils import BaseOutput, is_torch_version
  from ..utils.torch_utils import randn_tensor
  from .activations import get_activation
  from .attention_processor import SpatialNorm
  from .unet_2d_blocks import AutoencoderTinyBlock, UNetMidBlock2D, get_down_block, get_up_block
label_desc: Use image node_id 1 to create a new UpSample object from inherited base classes: nn with example: obj = UpSample(in_channels, out_channels)