code: def _computed_output_shape(self) -> torch.Size:
      dummy_input = torch.rand(2, *self.input_shape, device=self.device)
      output_tensor = self.forward(dummy_input.type(self.input_dtype))
  
      if isinstance(output_tensor, torch.Tensor):
          return output_tensor.size()[1:]
      elif (
          isinstance(output_tensor, dict)
          and ENCODER_OUTPUT in output_tensor
      ):
          return output_tensor[ENCODER_OUTPUT].size()[1:]
      else:
          raise ValueError("Unknown output tensor type.")
  
imports: import math
  import os
  import warnings
  from abc import abstractmethod
  from functools import lru_cache
  from typing import List, Optional, Tuple, Union
  import torch
  from torch import nn
  from torch.nn import Module, ModuleDict
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import ENCODER_OUTPUT
  from ludwig.utils.strings_utils import SpecialSymbol
label_desc: Use image node_id 9 for calling the LudwigModule obj's underlying member method code with example usage: obj._computed_output_shape() and returns: output_tensor, output_tensor