code: def forward(self, x):
      # Clamp.
      x = torch.tanh(x / 3) * 3
  
      if self.training and self.gradient_checkpointing:
  
          def create_custom_forward(module):
              def custom_forward(*inputs):
                  return module(*inputs)
  
              return custom_forward
  
          if is_torch_version(">=", "1.11.0"):
              x = torch.utils.checkpoint.checkpoint(
                  create_custom_forward(self.layers),
                  x,
                  use_reentrant=False,
              )
          else:
              x = torch.utils.checkpoint.checkpoint(
                  create_custom_forward(self.layers), x
              )
  
      else:
          x = self.layers(x)
  
      # scale image from [0, 1] to [-1, 1] to match diffusers convention
      return x.mul(2).sub(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 2 for calling the DecoderTiny obj's underlying member method code with example usage: obj.forward(x) and returns: x, custom_forward, module