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 __step__(self, batch, mode="train"):
      edge_index, edge_type, edge_type_range = batch
      loss, auprc, auroc, ap = self.forward(
          edge_index.reshape((2, -1)),
          edge_type.flatten(),
          edge_type_range.reshape((-1, 2)),
      )
  
      if mode == "train" or "val":
          self.log(f"{mode}_loss", loss)
      else:
          self.log(f"{mode}_auprc", auprc)
          self.log(f"{mode}_auroc", auroc)
          self.log(f"{mode}_ap@50", ap)
  
      return loss
  
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 5 for calling the GripNetLinkPrediction obj's underlying member method code with example usage: obj.__step__(batch, mode) and returns: loss