class_docstr: The View Correlation Discovery Network (VCDN) to learn the higher-level intra-view and cross-view correlations
  in the label space, implemented according to the method described in 'MOGONET integrates multi-omics data using
  graph convolutional networks allowing patient classification and biomarker identification'
  - Wang, T., Shao, W., Huang, Z., Tang, H., Zhang, J., Ding, Z., Huang, K. (2021).
  
  Args:
      num_modalities (int): The total number of modalities in the dataset.
      num_classes (int): The total number of classes in the dataset.
      hidden_dim (int): Size of the hidden layer.
code: def forward(
      self, multimodal_input: List[torch.Tensor]
  ) -> torch.Tensor:
      for modality in range(self.num_modalities):
          multimodal_input[modality] = torch.sigmoid(
              multimodal_input[modality]
          )
      x = torch.reshape(
          torch.matmul(
              multimodal_input[0].unsqueeze(-1),
              multimodal_input[1].unsqueeze(1),
          ),
          (-1, pow(self.num_classes, 2), 1),
      )
      for modality in range(2, self.num_modalities):
          x = torch.reshape(
              torch.matmul(x, multimodal_input[modality].unsqueeze(1)),
              (-1, pow(self.num_classes, modality + 1), 1),
          )
      input_tensor = torch.reshape(
          x, (-1, pow(self.num_classes, self.num_modalities))
      )
      output = self.model(input_tensor)
  
      return output
  
imports: from typing import List, Tuple
  import numpy
  import pytorch_lightning
  import torch
  import torch.nn
  import torch.nn.functional
  from kale.embed.gripnet import GripNet
  from kale.evaluate.metrics import auprc_auroc_ap
  from kale.prepdata.graph_negative_sampling import typed_negative_sampling
  from kale.prepdata.supergraph_construct import SuperGraph
  from kale.utils.initialize_nn import bias_init, xavier_init
label_desc: Use image node_id 3 for calling the VCDN obj's underlying member method code with example usage: obj.forward(multimodal_input) and returns: output