docstr: Processes single user_idx from test set and returns numpy list of scores
  for all items.
  
  Parameters
  ----------
  train_set : cornac.data.dataset.Dataset
      The train set as provided by cornac
  
  Returns
  -------
  test_pred_ratings : numpy.array
      Numpy array containing all ratings for the given user_idx.
docstr_tok: ["Processes","single","user_idx","from","test","set","and","returns","numpy","list","of","scores","for","all","items",".","Parameters","--","--","--","--","--","train_set",":","cornac.data.dataset.Dataset","The","train","set","as","provided","by","cornac","Returns","--","--","--","-","test_pred_ratings",":","numpy.array","Numpy","array","containing","all","ratings","for","the","given","user_idx","."]
code: def predict_one(self, user_idx):
      """
      Processes single user_idx from test set and returns numpy list of scores
      for all items.
  
      Parameters
      ----------
      train_set : cornac.data.dataset.Dataset
          The train set as provided by cornac
  
      Returns
      -------
      test_pred_ratings : numpy.array
          Numpy array containing all ratings for the given user_idx.
      """
      test_dec_graph = self._generate_test_user_graph(user_idx)
      test_dec_graph = test_dec_graph.int().to(self.device)
  
      self.net.eval()
  
      with torch.no_grad():
          pred_ratings = self.net(self.train_enc_graph, test_dec_graph)
  
      nd_positive_rating_values = torch.FloatTensor(
          self.rating_values
      ).to(self.device)
  
      test_pred_ratings = (
          torch.softmax(pred_ratings, dim=1)
          * nd_positive_rating_values.view(1, -1)
      ).sum(dim=1)
  
      return test_pred_ratings.cpu().numpy()
  
imports: import logging
  import time
  from collections import defaultdict
  import torch
  from torch import nn
  import dgl
  import numpy
  import scipy.sparse
  from tqdm import tqdm
  from .nn_modules import NeuralNetwork
  from .utils import get_optimizer, torch_net_info, torch_total_param_num
label_desc: Use image node_id 10 for calling the Model obj's underlying member method code with example usage: obj.predict_one(user_idx) and returns: test_pred_ratings