code: def matching_probas(
      embeddings, targets, test_embeddings, num_classes, eps=1e-8
  ):
      """Computes the probability of test samples given the training dataset for
      the matching network [1].
  
      Parameters
      ----------
      embeddings : `torch.Tensor` instance
          A tensor containing the embeddings of the train/support inputs. This
          tensor has shape `(batch_size, num_train_samples, embedding_size)`.
  
      targets : `torch.LongTensor` instance
          A tensor containing the targets of the train/support dataset. This tensor
          has shape `(batch_size, num_train_samples)`.
  
      test_embeddings : `torch.Tensor` instance
          A tensor containing the embeddings of the test/query inputs. This tensor
          has shape `(batch_size, num_test_samples, embedding_size)`.
  
      num_classes : int
          Number of classes (i.e. `N` in "N-way classification") in the
          classification task.
  
      eps : float (default: 1e-8)
          Small value to avoid division by zero.
  
      Returns
      -------
      probas : `torch.Tensor` instance
          A tensor containing the probabilities of the test samples given the
          training dataset for the matching network. This tensor has shape
          `(batch_size, num_classes, num_test_samples)`.
  
      References
      ----------
      .. [1] Vinyals, O., Blundell, C., Lillicrap, T. and Wierstra, D. (2016).
             Matching Networks for One Shot Learning. In Advances in Neural
             Information Processing Systems (pp. 3630-3638) (https://arxiv.org/abs/1606.04080)
      """
      log_probas = matching_log_probas(
          embeddings, targets, test_embeddings, num_classes, eps=eps
      )
      return log_probas.exp()
  
imports: import torch
  import torch.nn.functional
label_desc: Use image node_id 3 for calling a global function with example usage: matching_probas(embeddings, targets, test_embeddings, num_classes, eps) and returns: log_probas