code: def __init__(
      self,
      input_entity_dim,
      entity_dim,
      input_relation_dim,
      relation_dim,
  ):
      super(MLP, self).__init__()
      self.transform_e_net = torch.nn.Linear(
          input_entity_dim, entity_dim
      )
      self.transform_r_net = torch.nn.Linear(
          input_relation_dim, relation_dim
      )
      self.reset_parameters()
  
imports: import os
  import numpy
  import math
  import dgl.backend
  import pdb
  import torch
  import torch.nn
  from .pytorch.tensor_models import logsigmoid
  from .pytorch.tensor_models import abs
  from .pytorch.tensor_models import masked_select
  from .pytorch.tensor_models import get_device, get_dev
  from .pytorch.tensor_models import norm
  from .pytorch.tensor_models import get_scalar
  from .pytorch.tensor_models import reshape
  from .pytorch.tensor_models import cuda
  from .pytorch.tensor_models import ExternalEmbedding
  from .pytorch.tensor_models import InferEmbedding
  from .pytorch.score_fun import *
  from .pytorch.loss import LossGenerator
label_desc: Use image node_id 1 to create a new MLP object from inherited base classes: torch.nn with example: obj = MLP(input_entity_dim, entity_dim, input_relation_dim, relation_dim)