Fix: keras.ops.quantile works with tf graph execution (#21782)

A `deque` failing to be converted to a tensor in the transpose call in `keras.ops.quantile` caused errors when running in tf graph contexts. By constructing a list from the deque before passing it to the `transpose` call we avoid this error.

Author: SamKnightGit

keras/src/backend/tensorflow/numpy.py

@@ -2301,7 +2301,7 @@ def _get_indices(method):
  return gathered_y
  perm = collections.deque(range(ndims))
  perm.rotate(shift_value_static)
- return tf.transpose(a=gathered_y, perm=perm)
+ return tf.transpose(a=gathered_y, perm=list(perm))
 
 
 def quantile(x, q, axis=None, method="linear", keepdims=False):

keras/src/ops/numpy_test.py

@@ -3325,6 +3325,24 @@ def test_quantile(self):
  np.quantile(x, q, axis=1, method=method),
  )
 
+ @pytest.mark.skipif(
+ backend.backend() != "tensorflow",
+ reason="Only test tensorflow backend",
+ )
+ def test_quantile_in_tf_function(self):
+ import tensorflow as tf
+
+ x = knp.array([[1, 2, 3], [4, 5, 6]])
+ q = [0.5]
+ expected_output = np.array([[2, 5]])
+
+ @tf.function
+ def run_quantile(x, q, axis):
+ return knp.quantile(x, q, axis=axis)
+
+ result = run_quantile(x, q, axis=1)
+ self.assertAllClose(result, expected_output)
+
  def test_take(self):
  x = np.arange(24).reshape([1, 2, 3, 4])
  indices = np.array([0, 1])