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 __init__(
      self, in_dim: int, out_dim: int, bias: bool = True
  ) -> None:
      super().__init__()
      self.fc = nn.Linear(in_dim, out_dim, bias=bias)
      self.reset_parameters()
  
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 1 to create a new LinearClassifier object from inherited base classes: nn with example: obj = LinearClassifier(in_dim, out_dim, bias)