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.
docstr: Initialize the parameters of the model.
docstr_tok: ["Initialize","the","parameters","of","the","model","."]
code: def reset_parameters(self) -> None:
      """Initialize the parameters of the model."""
      self.model.apply(xavier_init)
      self.model.apply(bias_init)
  
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 2 for calling the VCDN obj's underlying member method code with example usage: obj.reset_parameters() without return types