class_docstr: KTUP is a knowledge-based recommendation model. It adopts the strategy of multi-task learning to jointly learn
  recommendation and KG-related tasks, with the goal of understanding the reasons that a user interacts with an item.
  This method utilizes an attention mechanism to combine all preferences into a single-vector representation.
docstr: Args:
      indices (Variable): A vector containing indices,
          whose size is (batch_size,).
      num_classes (Variable): The number of classes, which would be
          the second dimension of the resulting one-hot matrix.
  
  Returns:
      torch.Tensor: The one-hot matrix of size (batch_size, num_classes).
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code: def convert_to_one_hot(self, indices, num_classes):
      r"""
      Args:
          indices (Variable): A vector containing indices,
              whose size is (batch_size,).
          num_classes (Variable): The number of classes, which would be
              the second dimension of the resulting one-hot matrix.
  
      Returns:
          torch.Tensor: The one-hot matrix of size (batch_size, num_classes).
      """
  
      old_shape = indices.shape
      new_shape = torch.Size([i for i in old_shape] + [num_classes])
      indices = indices.unsqueeze(len(old_shape))
  
      one_hot = Variable(
          indices.data.new(new_shape)
          .zero_()
          .scatter_(len(old_shape), indices.data, 1)
      )
      return one_hot
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
  from recbole.model.abstract_recommender import KnowledgeRecommender
  from recbole.model.init import xavier_uniform_initialization
  from recbole.model.loss import BPRLoss, EmbMarginLoss
  from recbole.utils import InputType
label_desc: Use image node_id 3 for calling the KTUP obj's underlying member method code with example usage: obj.convert_to_one_hot(indices, num_classes) and returns: one_hot