Update Torch ops.array (#375)

* Update Torch ops.array

This was previously inconsistent with Jax and TF, where ops.array produces an array/tensor of the native backend type. Instead this produced a NumPy array

* Add ops test

* Fix metrics tests

* Update torch np.tile implementation

keras_core/backend/torch/numpy.py

@@ -4,6 +4,7 @@
 from keras_core.backend.torch.core import cast
 from keras_core.backend.torch.core import convert_to_tensor
 from keras_core.backend.torch.core import get_device
+from keras_core.backend.torch.core import is_tensor
 from keras_core.backend.torch.core import to_torch_dtype
 
 TORCH_INT_TYPES = (
@@ -187,9 +188,9 @@ def argsort(x, axis=-1):
 
 def array(x, dtype=None):
     dtype = to_torch_dtype(dtype)
-    if not isinstance(x, torch.Tensor):
+    if isinstance(x, torch.Tensor):
         return x
-    return x.numpy()
+    return torch.tensor(x, dtype=dtype)
 
 
 def average(x, axis=None, weights=None):
@@ -754,6 +755,8 @@ def round(x, decimals=0):
 
 
 def tile(x, repeats):
+    if is_tensor(repeats):
+        repeats = tuple(repeats.int().numpy())
     x = convert_to_tensor(x)
     return torch.tile(x, dims=repeats)
 

keras_core/metrics/metrics_utils.py

@@ -509,7 +509,7 @@ def update_confusion_matrix_variables(
         data_tiles = [num_thresholds, 1]
 
     thresh_tiled = ops.tile(
-        ops.reshape(thresholds, thresh_pretile_shape), ops.array(thresh_tiles)
+        ops.reshape(thresholds, thresh_pretile_shape), thresh_tiles
     )
 
     # Tile the predictions for every threshold.

keras_core/operations/numpy_test.py

@@ -2242,6 +2242,8 @@ def test_array(self):
         x = np.array([[1, 2, 3], [3, 2, 1]])
         self.assertAllClose(knp.array(x), np.array(x))
         self.assertAllClose(knp.Array()(x), np.array(x))
+        self.assertTrue(backend.is_tensor(knp.array(x)))
+        self.assertTrue(backend.is_tensor(knp.Array()(x)))
 
     def test_average(self):
         x = np.array([[1, 2, 3], [3, 2, 1]])