I’m not completely sure, if you are trying to pass the raw logits/probabilities to the classification_report, but as the docs explain a 1D array containing the predictions is expected.
Here is a small example for 10 classes using 100 samples:
y = np.random.randint(0, 10, (100,))
pred = np.random.randint(0, 10, (100,))

print(classification_report(y, pred))
>               precision    recall  f1-score   support
  
             0       0.12      0.08      0.10        13
             1       0.00      0.00      0.00        10
             2       0.20      0.10      0.13        10
             3       0.07      0.08      0.07        12
             4       0.38      0.27      0.32        11
             5       0.07      0.07      0.07        14
             6       0.29      0.29      0.29         7
             7       0.00      0.00      0.00         4
             8       0.08      0.11      0.10         9
             9       0.17      0.10      0.12        10

      accuracy                           0.11       100
     macro avg       0.14      0.11      0.12       100
  weighted avg       0.14      0.11      0.12       100

If you are dealing with a multi-class classification and your model outputs logits or (log)probabilities, use preds = torch.argmax(output, dim=1) to get the predicted class labels.
Afterwards flatten both tensors, if the method raises a shape error.