class_docstr: Build a linear transformation module.
  
  Args:
      in_dim (int): Size of each input sample.
      out_dim (int): Size of each output sample.
      bias (bool, optional): If set to ``False``, the layer will not learn an additive bias. (default: ``True``)
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.fc.apply(xavier_init)
      self.fc.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 LinearClassifier obj's underlying member method code with example usage: obj.reset_parameters() without return types