code: def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
      x = torch.relu(x)
      x = self.deconv(x)
      return x
  
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 2 for calling the UpSample obj's underlying member method code with example usage: obj.forward(x) and returns: x