class_docstr: Build GripNet-DistMult (encoder-decoder) model for link prediction.
  
  Args:
      supergraph (SuperGraph): the input supergraph.
      learning_rate (float): the learning rate for training.
      epsilon (float, optional): a small number in loss function to improve numerical stability. Defaults to 1e-13.
code: def __init__(
      self,
      supergraph: SuperGraph,
      learning_rate: float,
      epsilon: float = 1e-13,
  ):
      super().__init__()
  
      self.learning_rate = learning_rate
      self.epsilon = epsilon
  
      self.encoder = GripNet(supergraph)
      self.decoder = self.__init_decoder__()
  
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 GripNetLinkPrediction object from inherited base classes: pl with example: obj = GripNetLinkPrediction(supergraph, learning_rate, epsilon)