text: import torch
  
  
  def get_accuracy(prototypes, embeddings, targets):
      """Compute the accuracy of the prototypical network on the test/query points.
  
      Parameters
      ----------
      prototypes : `torch.FloatTensor` instance
          A tensor containing the prototypes for each class. This tensor has shape 
          `(meta_batch_size, num_classes, embedding_size)`.
      embeddings : `torch.FloatTensor` instance
          A tensor containing the embeddings of the query points. This tensor has 
          shape `(meta_batch_size, num_examples, embedding_size)`.
      targets : `torch.LongTensor` instance
          A tensor containing the targets of the query points. This tensor has 
          shape `(meta_batch_size, num_examples)`.
  
      Returns
      -------
      accuracy : `torch.FloatTensor` instance
          Mean accuracy on the query points.
      """
      sq_distances = torch.sum((prototypes.unsqueeze(1)
          - embeddings.unsqueeze(2)) ** 2, dim=-1)
      _, predictions = torch.min(sq_distances, dim=-1)
      return torch.mean(predictions.eq(targets).float())
  