class_docstr: The `UpSample` layer of a variational autoencoder that upsamples its input.
  
  Args:
      in_channels (`int`, *optional*, defaults to 3):
          The number of input channels.
      out_channels (`int`, *optional*, defaults to 3):
          The number of output channels.
docstr: The forward method of the `UpSample` class.
docstr_tok: ["The","forward","method","of","the","`","UpSample","`","class","."]
code: def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
      r"""The forward method of the `UpSample` class."""
      x = torch.relu(x)
      x = self.deconv(x)
      return x
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple
  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