Fix int16/float16 support in ops.arange in TF.

keras/backend/tensorflow/numpy.py

@@ -409,7 +409,13 @@ def arange(start, stop=None, step=1, dtype=None):
             dtypes_to_resolve.append(getattr(stop, "dtype", type(stop)))
         dtype = dtypes.result_type(*dtypes_to_resolve)
     dtype = standardize_dtype(dtype)
-    return tf.range(start, stop, delta=step, dtype=dtype)
+    try:
+        out = tf.range(start, stop, delta=step, dtype=dtype)
+    except tf.errors.NotFoundError:
+        # Some dtypes may not work in eager mode on CPU or GPU.
+        out = tf.range(start, stop, delta=step, dtype="float32")
+        out = tf.cast(out, dtype)
+    return out
 
 
 @sparse.densifying_unary(0.5 * np.pi)

keras/ops/numpy_test.py

@@ -4929,6 +4929,8 @@ def test_argsort(self, dtype):
         (0.0, 10, 1, None),
         (10, None, 1, "float32"),
         (10, None, 1, "int32"),
+        (10, None, 1, "int16"),
+        (10, None, 1, "float16"),
     )
     def test_arange(self, start, stop, step, dtype):
         import jax.numpy as jnp