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``)
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      x = self.fc(x)
      return x
  
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 LinearClassifier obj's underlying member method code with example usage: obj.forward(x) and returns: x