code: def compute_th_y_true_y_logits(y_true, y_pred):
      # Create torch.tensor from numpy
      th_y_true = torch.from_numpy(y_true).unsqueeze(0)
      # Create logits torch.tensor:
      num_classes = max(np.max(y_true), np.max(y_pred)) + 1
      y_probas = np.ones((num_classes,) + y_true.shape) * -10
      for i in range(num_classes):
          y_probas[i, (y_pred == i)] = 720
      th_y_logits = torch.from_numpy(y_probas).unsqueeze(0)
      return th_y_true, th_y_logits
  
imports: import os
  import numpy
  import pytest
  import torch
  from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score
  import ignite.distributed
  from ignite.exceptions import NotComputableError
  from ignite.metrics import ConfusionMatrix, IoU, JaccardIndex, mIoU
  from ignite.metrics.confusion_matrix import cmAccuracy, cmPrecision, cmRecall, DiceCoefficient
label_desc: Use image node_id 8 for calling a global function with example usage: compute_th_y_true_y_logits(y_true, y_pred) and returns: th_y_true,  th_y_logits