code: def test_mask(self):
      mask = np.array([True, False, True, True])
      y_true = np.array([1.0, 0.0, 1.0, 0.0])
      y_pred = np.array([0.1, 0.2, 0.3, 0.4])
  
      masked_y_true = np.array([1.0, 1.0, 0.0])
      masked_y_pred = np.array([0.1, 0.3, 0.4])
  
      mask = ops.convert_to_tensor(mask)
      y_true = ops.convert_to_tensor(y_true)
      y_pred = ops.convert_to_tensor(y_pred)
      y_pred._keras_mask = mask
  
      loss_fn = ExampleLoss()
      loss = loss_fn(y_true, y_pred)
      self.assertEqual(backend.standardize_dtype(loss.dtype), "float32")
      self.assertAllClose(
          np.sum((masked_y_true - masked_y_pred) ** 2) / 3, loss
      )
  
      # Test edge case where everything is masked.
      mask = np.array([False, False, False, False])
      y_pred._keras_mask = mask
      loss = loss_fn(y_true, y_pred)
      self.assertEqual(backend.standardize_dtype(loss.dtype), "float32")
      self.assertAllClose(loss, 0)
  
imports: import numpy
  import pytest
  from keras import backend
  from keras import losses
  from keras import ops
  from keras import testing
  from keras.losses.loss import Loss
label_desc: Use image node_id 2 for calling the LossTest obj's underlying member method code with example usage: obj.test_mask() without return types