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_decoder__(self) -> DistMultDecoder:
      in_channels = self.encoder.out_channels
      supergraph = self.encoder.supergraph
      task_supervertex_name = supergraph.topological_order[-1]
      num_edge_type = supergraph.supervertex_dict[
          task_supervertex_name
      ].num_edge_type
  
      # get the number of nodes on the task-associated supervertex
      self.num_task_nodes = supergraph.supervertex_dict[
          task_supervertex_name
      ].num_node
  
      return DistMultDecoder(in_channels, num_edge_type)
  
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 GripNetLinkPrediction obj's underlying member method code with example usage: obj.__init_decoder__() and returns: DistMultDecoder