code: def forward(
      self, input: Tensor, output_size: t.Optional[t.List[int]] = None
  ) -> Tensor:
      w = self.wrappee
      if w.padding_mode != "zeros":
          raise ValueError(
              "Only `zeros` padding mode is supported for ConvTranspose2d"
          )
  
      assert isinstance(w.padding, tuple)
      # One cannot replace List by Tuple or Sequence in "_output_padding"
      # because torch.Script does not support `Sequence[T]` or
      # `Tuple[T, ...]`.
      output_padding = w._output_padding(
          input,
          output_size,
          w.stride,
          w.padding,
          w.kernel_size,
          w.dilation,
      )  # type: ignore[arg-type]
  
      return F.conv_transpose2d(
          input,
          self.available_weights(),
          w.bias,
          w.stride,
          w.padding,
          output_padding,
          w.groups,
          w.dilation,
      )
  
imports: import typing
  import torch
  import torch.nn
  from torch import Tensor
  from torch.nn import functional
  from abc import ABC, abstractmethod
  from enum import Enum
  from avalanche.core import BaseSGDPlugin, Template
  from avalanche.models.dynamic_modules import MultiTaskModule
  from avalanche.models.simple_mlp import SimpleMLP
  from typing import Union
  from avalanche.training.templates.base_sgd import BaseSGDTemplate
label_desc: Use image node_id 2 for calling the _PnConvTransposedNd obj's underlying member method code with example usage: obj.forward(input, output_size) and returns: F