Removed workaround for not being able to use tf.math.mod as it works …

…now.

PiperOrigin-RevId: 258793478

tensorflow_graphics/math/interpolation/bspline.py

@@ -30,11 +30,6 @@
 from tensorflow_graphics.util import shape
 
 
-# TODO(b/131510643): remove when TF API is
-def _mod(x, y):
-  return x - tf.cast((x // y) * y, dtype=x.dtype)
-
-
 class Degree(enum.IntEnum):
   """Defines valid degrees for B-spline interpolation."""
   CONSTANT = 0
@@ -125,7 +120,7 @@ def knot_weights(positions,
       knots with nonzero weights. If set to True, the function returns the
       weights of only the `degree` + 1 knots that are non-zero, as well as the
       indices of the knots.
-    name: A name for this op. Defaults to "bsplines_knot_weights".
+    name: A name for this op. Defaults to "bspline_knot_weights".
 
   Returns:
     A tensor with dense weights for each control point, with the shape
@@ -138,7 +133,7 @@ def knot_weights(positions,
     ValueError: If degree is greater than 4 or num_knots - 1, or less than 0.
     InvalidArgumentError: If positions are not in the right range.
   """
-  with tf.compat.v1.name_scope(name, "bsplines_knot_weights", [positions]):
+  with tf.compat.v1.name_scope(name, "bspline_knot_weights", [positions]):
     positions = tf.convert_to_tensor(value=positions)
 
     if degree > 4 or degree < 0:
@@ -198,7 +193,7 @@ def knot_weights(positions,
         shape=(-1,))
     ind_col = tf.reshape(ind_col, shape=(-1,)) + tiled_shifts
     if cyclical:
-      ind_col = _mod(ind_col, num_knots)
+      ind_col = tf.math.mod(ind_col, num_knots)
     indices = tf.stack((tf.reshape(ind_row, shape=(-1,)), ind_col), axis=-1)
     shape_indices = tf.concat((tf.reshape(
         num_positions, shape=(1,)), tf.constant(
@@ -225,7 +220,7 @@ def interpolate_with_weights(knots, weights, name=None):
       `C` is the number of knots.
     weights: A tensor with shape `[A1, ..., An, C]` containing dense weights for
       the knots, where `C` is the number of knots.
-    name: A name for this op. Defaults to "bsplines_interpolate_with_weights".
+    name: A name for this op. Defaults to "bspline_interpolate_with_weights".
 
   Returns:
     A tensor with shape `[A1, ..., An, B1, ..., Bk]`, which is the result of
@@ -234,7 +229,7 @@ def interpolate_with_weights(knots, weights, name=None):
   Raises:
     ValueError: If the last dimension of knots and weights is not equal.
   """
-  with tf.compat.v1.name_scope(name, "bsplines_interpolate_with_weights",
+  with tf.compat.v1.name_scope(name, "bspline_interpolate_with_weights",
                                [knots, weights]):
     knots = tf.convert_to_tensor(value=knots)
     weights = tf.convert_to_tensor(value=weights)