Replace # pytype: disable suppressions with # pyrefly: ignore

tensorflow_graphics/geometry/convolution/graph_convolution.py

@@ -96,7 +96,7 @@ def feature_steered_convolution(
     ValueError: if the input dimensions are invalid.
   """
   #  pyformat: enable
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     data = tf.convert_to_tensor(value=data)
     neighbors = tf.compat.v1.convert_to_tensor_or_sparse_tensor(value=neighbors)
     if sizes is not None:
@@ -239,7 +239,7 @@ def edge_convolution_template(
     ValueError: if the input dimensions are invalid.
   """
   #  pyformat: enable
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     data = tf.convert_to_tensor(value=data)
     neighbors = tf.compat.v1.convert_to_tensor_or_sparse_tensor(value=neighbors)
     if sizes is not None:

tensorflow_graphics/geometry/convolution/graph_pooling.py

@@ -74,7 +74,7 @@ def pool(data: type_alias.TensorLike,
     ValueError: if `algorithm` is invalid.
   """
   #  pyformat: enable
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     data = tf.convert_to_tensor(value=data)
     pool_map = tf.compat.v1.convert_to_tensor_or_sparse_tensor(value=pool_map)
     if sizes is not None:
@@ -161,7 +161,7 @@ def unpool(data: type_alias.TensorLike,
     ValueError: if the input dimensions are invalid.
   """
   #  pyformat: enable
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     data = tf.convert_to_tensor(value=data)
     pool_map = tf.compat.v1.convert_to_tensor_or_sparse_tensor(value=pool_map)
     if sizes is not None:
@@ -253,7 +253,7 @@ def upsample_transposed_convolution(
     ValueError: if the input dimensions are invalid.
   """
   #  pyformat: enable
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     data = tf.convert_to_tensor(value=data)
     pool_map = tf.compat.v1.convert_to_tensor_or_sparse_tensor(value=pool_map)
     if sizes is not None:

tensorflow_graphics/geometry/convolution/utils.py

@@ -249,7 +249,7 @@ def flatten_batch_to_2d(data: type_alias.TensorLike,
   Raises:
     ValueError: if the input tensor dimensions are invalid.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     data = tf.convert_to_tensor(value=data)
     if sizes is not None:
       sizes = tf.convert_to_tensor(value=sizes)
@@ -272,7 +272,7 @@ def flatten_batch_to_2d(data: type_alias.TensorLike,
 
       def unflatten(flat, name="utils_unflatten"):
         """Invert flatten_batch_to_2d."""
-        with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+        with tf.name_scope(name):
           flat = tf.convert_to_tensor(value=flat)
           output_shape = tf.concat((data_shape[:-1], tf.shape(input=flat)[-1:]),
                                    axis=0)
@@ -288,7 +288,7 @@ def unflatten(flat, name="utils_unflatten"):
 
       def unflatten(flat, name="utils_unflatten"):
         """Invert flatten_batch_to_2d."""
-        with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+        with tf.name_scope(name):
           flat = tf.convert_to_tensor(value=flat)
           output_shape = tf.concat((data_shape[:-1], tf.shape(input=flat)[-1:]),
                                    axis=0)
@@ -362,7 +362,7 @@ def unflatten_2d_to_batch(data: type_alias.TensorLike,
   Returns:
     A tensor with shape `[A1, A2, ..., max_rows, D2]`.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     data = tf.convert_to_tensor(value=data)
     sizes = tf.convert_to_tensor(value=sizes)
     if max_rows is None:
@@ -418,7 +418,7 @@ def convert_to_block_diag_2d(data: tf.sparse.SparseTensor,
     TypeError: if the input types are invalid.
     ValueError: if the input dimensions are invalid.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     data = tf.compat.v1.convert_to_tensor_or_sparse_tensor(value=data)
     if sizes is not None:
       sizes = tf.convert_to_tensor(value=sizes)

tensorflow_graphics/geometry/representation/grid.py

@@ -98,7 +98,7 @@ def generate(starts, stops, nums, name="grid_generate"):
     axis and from -2.0 to 2.0 with 5 subdivisions for the y axis. This lead to a
     tensor of shape (3, 5, 2).
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     starts = tf.convert_to_tensor(value=starts)
     stops = tf.convert_to_tensor(value=stops)
     nums = tf.convert_to_tensor(value=nums)

tensorflow_graphics/geometry/representation/point.py

@@ -55,7 +55,7 @@ def distance_to_ray(point: type_alias.TensorLike,
     ValueError: If the shape of `point`, `origin`, or 'direction' is not
     supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     point = tf.convert_to_tensor(value=point)
     origin = tf.convert_to_tensor(value=origin)
     direction = tf.convert_to_tensor(value=direction)
@@ -99,7 +99,7 @@ def project_to_ray(point: type_alias.TensorLike,
     ValueError: If the shape of `point`, `origin`, or 'direction' is not
     supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     point = tf.convert_to_tensor(value=point)
     origin = tf.convert_to_tensor(value=origin)
     direction = tf.convert_to_tensor(value=direction)

tensorflow_graphics/geometry/representation/ray.py

@@ -92,7 +92,7 @@ def sample_1d(
     A tensor of shape `[A1, ..., An, M, 3]` indicating the M points on the ray
       and a tensor of shape `[A1, ..., An, M]` for the Z values on the points.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     ray_org = tf.convert_to_tensor(ray_org)
     ray_dir = tf.convert_to_tensor(ray_dir)
     near = tf.convert_to_tensor(near) * tf.ones((1,))
@@ -175,7 +175,7 @@ def sample_stratified_1d(
     A tensor of shape `[A1, ..., An, M, 3]` indicating the M points on the ray
       and a tensor of shape `[A1, ..., An, M]` for the Z values on the points.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     ray_org = tf.convert_to_tensor(ray_org)
     ray_dir = tf.convert_to_tensor(ray_dir)
     near = tf.convert_to_tensor(near) * tf.ones((1,))
@@ -245,7 +245,7 @@ def sample_inverse_transform_stratified_1d(
     A tensor of shape `[A1, ..., An, M, 3]` indicating the M points on the ray
       and a tensor of shape `[A1, ..., An, M]` for the Z values on the points.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     shape.check_static(
         tensor=ray_org,
         tensor_name="ray_org",
@@ -312,7 +312,7 @@ def triangulate(startpoints, endpoints, weights, name="ray_triangulate"):
   Raises:
     ValueError: If the shape of the arguments is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     startpoints = tf.convert_to_tensor(value=startpoints)
     endpoints = tf.convert_to_tensor(value=endpoints)
     weights = tf.convert_to_tensor(value=weights)
@@ -403,7 +403,7 @@ def intersection_ray_sphere(sphere_center,
       `point_on_ray` is not supported.
     tf.errors.InvalidArgumentError: If `ray` is not normalized.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     sphere_center = tf.convert_to_tensor(value=sphere_center)
     sphere_radius = tf.convert_to_tensor(value=sphere_radius)
     ray = tf.convert_to_tensor(value=ray)

tensorflow_graphics/geometry/representation/triangle.py

@@ -57,7 +57,7 @@ def normal(v0: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `v0`, `v1`, or `v2` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     v0 = tf.convert_to_tensor(value=v0)
     v1 = tf.convert_to_tensor(value=v1)
     v2 = tf.convert_to_tensor(value=v2)
@@ -104,7 +104,7 @@ def area(v0: type_alias.TensorLike,
     A tensor of shape `[A1, ..., An, 1]`, where the last dimension represents
       a normalized vector.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     v0 = tf.convert_to_tensor(value=v0)
     v1 = tf.convert_to_tensor(value=v1)
     v2 = tf.convert_to_tensor(value=v2)

tensorflow_graphics/image/matting.py

@@ -131,7 +131,7 @@ def build_matrices(image: type_alias.TensorLike,
   Raises:
     ValueError: If `image` is not of rank 4.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     image = tf.convert_to_tensor(value=image)
     eps = tf.constant(value=eps, dtype=image.dtype)
 
@@ -192,7 +192,7 @@ def linear_coefficients(matte: type_alias.TensorLike,
     of rank 4. If `pseudo_inverse` is not of rank 5. If `B` is different
     between `matte` and `pseudo_inverse`.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     matte = tf.convert_to_tensor(value=matte)
     pseudo_inverse = tf.convert_to_tensor(value=pseudo_inverse)
 
@@ -216,7 +216,7 @@ def linear_coefficients(matte: type_alias.TensorLike,
     width = tf.shape(input=matte)[2] + size - 1
     coeffs = tf.image.resize_with_crop_or_pad(coeffs, height, width)
     ones = tf.image.resize_with_crop_or_pad(ones, height, width)
-    coeffs = _image_average(coeffs, size) / _image_average(ones, size)  # pyrefly: ignore[unsupported-operation]
+    coeffs = _image_average(coeffs, size) / _image_average(ones, size)
     return tf.split(coeffs, (-1, 1), axis=-1)
 
 
@@ -246,7 +246,7 @@ def loss(matte: type_alias.TensorLike,
     same size. If `laplacian` is not of rank 5. If `B` is different
     between `matte` and `laplacian`.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     matte = tf.convert_to_tensor(value=matte)
     laplacian = tf.convert_to_tensor(value=laplacian)
 
@@ -287,7 +287,7 @@ def reconstruct(image: type_alias.TensorLike,
     the last dimension of `coeff_add` is not 1. If the batch dimensions of
     `image`, `coeff_mul`, and `coeff_add` do not match.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     image = tf.convert_to_tensor(value=image)
     coeff_mul = tf.convert_to_tensor(value=coeff_mul)
     coeff_add = tf.convert_to_tensor(value=coeff_add)

tensorflow_graphics/image/pyramid.py

@@ -170,7 +170,7 @@ def downsample(image: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `image` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     image = tf.convert_to_tensor(value=image)
 
     shape.check_static(tensor=image, tensor_name="image", has_rank=4)
@@ -196,7 +196,7 @@ def merge(levels: List[type_alias.TensorLike],
   Raises:
     ValueError: If the shape of the elements of `levels` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     levels = [tf.convert_to_tensor(value=level) for level in levels]
 
     for index, level in enumerate(levels):
@@ -229,7 +229,7 @@ def split(image: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `image` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     image = tf.convert_to_tensor(value=image)
 
     shape.check_static(tensor=image, tensor_name="image", has_rank=4)
@@ -264,7 +264,7 @@ def upsample(image: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `image` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     image = tf.convert_to_tensor(value=image)
 
     shape.check_static(tensor=image, tensor_name="image", has_rank=4)

tensorflow_graphics/image/transformer.py

@@ -77,7 +77,7 @@ def sample(image: type_alias.TensorLike,
     ValueError: If `image` has rank != 4. If `warp` has rank < 2 or its last
     dimension is not 2. If `image` and `warp` batch dimension does not match.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     image = tf.convert_to_tensor(value=image, name="image")
     warp = tf.convert_to_tensor(value=warp, name="warp")
 
@@ -154,7 +154,7 @@ def perspective_transform(
     its last two dimensions are not 3. If `image` and `transform_matrix` batch
     dimension does not match.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     image = tf.convert_to_tensor(value=image, name="image")
     transform_matrix = tf.convert_to_tensor(
         value=transform_matrix, name="transform_matrix")

tensorflow_graphics/math/interpolation/bspline.py

@@ -44,13 +44,13 @@ class Degree(enum.IntEnum):
 def _constant(position: tf.Tensor) -> tf.Tensor:
   """B-Spline basis function of degree 0 for positions in the range [0, 1]."""
   # A piecewise constant spline is discontinuous at the knots.
-  return tf.expand_dims(tf.clip_by_value(1.0 + position, 1.0, 1.0), axis=-1)  # pyrefly: ignore[unsupported-operation]
+  return tf.expand_dims(tf.clip_by_value(1.0 + position, 1.0, 1.0), axis=-1)
 
 
 def _linear(position: tf.Tensor) -> tf.Tensor:
   """B-Spline basis functions of degree 1 for positions in the range [0, 1]."""
   # Piecewise linear splines are C0 smooth.
-  return tf.stack((1.0 - position, position), axis=-1)  # pyrefly: ignore[unsupported-operation]
+  return tf.stack((1.0 - position, position), axis=-1)
 
 
 def _quadratic(position: tf.Tensor) -> tf.Tensor:
@@ -59,30 +59,30 @@ def _quadratic(position: tf.Tensor) -> tf.Tensor:
   pos_sq = tf.pow(position, 2.0)
 
   # Piecewise quadratic splines are C1 smooth.
-  return tf.stack((tf.pow(1.0 - position, 2.0) / 2.0, -pos_sq + position + 0.5,  # pyrefly: ignore[unsupported-operation]
+  return tf.stack((tf.pow(1.0 - position, 2.0) / 2.0, -pos_sq + position + 0.5,
                    pos_sq / 2.0),
                   axis=-1)
 
 
 def _cubic(position: tf.Tensor) -> tf.Tensor:
   """B-Spline basis functions of degree 3 for positions in the range [0, 1]."""
   # We pre-calculate the terms that are used multiple times.
-  neg_pos = 1.0 - position  # pyrefly: ignore[unsupported-operation]
+  neg_pos = 1.0 - position
   pos_sq = tf.pow(position, 2.0)
   pos_cb = tf.pow(position, 3.0)
 
   # Piecewise cubic splines are C2 smooth.
   return tf.stack(
       (tf.pow(neg_pos, 3.0) / 6.0, (3.0 * pos_cb - 6.0 * pos_sq + 4.0) / 6.0,
-       (-3.0 * pos_cb + 3.0 * pos_sq + 3.0 * position + 1.0) / 6.0,  # pyrefly: ignore[unsupported-operation]
+       (-3.0 * pos_cb + 3.0 * pos_sq + 3.0 * position + 1.0) / 6.0,
        pos_cb / 6.0),
       axis=-1)
 
 
 def _quartic(position: tf.Tensor) -> tf.Tensor:
   """B-Spline basis functions of degree 4 for positions in the range [0, 1]."""
   # We pre-calculate the terms that are used multiple times.
-  neg_pos = 1.0 - position  # pyrefly: ignore[unsupported-operation]
+  neg_pos = 1.0 - position
   pos_sq = tf.pow(position, 2.0)
   pos_cb = tf.pow(position, 3.0)
   pos_qt = tf.pow(position, 4.0)
@@ -92,8 +92,8 @@ def _quartic(position: tf.Tensor) -> tf.Tensor:
       (tf.pow(neg_pos, 4.0) / 24.0,
        (-4.0 * tf.pow(neg_pos, 4.0) + 4.0 * tf.pow(neg_pos, 3.0) +
         6.0 * tf.pow(neg_pos, 2.0) + 4.0 * neg_pos + 1.0) / 24.0,
-       (pos_qt - 2.0 * pos_cb - pos_sq + 2.0 * position) / 4.0 + 11.0 / 24.0,  # pyrefly: ignore[unsupported-operation]
-       (-4.0 * pos_qt + 4.0 * pos_cb + 6.0 * pos_sq + 4.0 * position + 1.0) /  # pyrefly: ignore[unsupported-operation]
+       (pos_qt - 2.0 * pos_cb - pos_sq + 2.0 * position) / 4.0 + 11.0 / 24.0,
+       (-4.0 * pos_qt + 4.0 * pos_cb + 6.0 * pos_sq + 4.0 * position + 1.0) /
        24.0, pos_qt / 24.0),
       axis=-1)
 
@@ -137,7 +137,7 @@ def knot_weights(
     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.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     positions = tf.convert_to_tensor(value=positions)
 
     if degree > 4 or degree < 0:
@@ -236,7 +236,7 @@ def interpolate_with_weights(
   Raises:
     ValueError: If the last dimension of knots and weights is not equal.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     knots = tf.convert_to_tensor(value=knots)
     weights = tf.convert_to_tensor(value=weights)
 
@@ -271,7 +271,7 @@ def interpolate(knots: type_alias.TensorLike,
     A tensor of shape `[A1, ... An, B1, ..., Bk]`, which is the result of spline
     interpolation.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     knots = tf.convert_to_tensor(value=knots)
     positions = tf.convert_to_tensor(value=positions)
 

tensorflow_graphics/math/interpolation/slerp.py

@@ -142,7 +142,7 @@ def interpolate_with_weights(
     A tensor of shape `[A1, ... , An, M]` containing the result of the
     interpolation.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     return weight1 * vector1 + weight2 * vector2
 
 
@@ -185,7 +185,7 @@ def quaternion_weights(
     Two tensors of shape `[A1, ... , An, 1]` each, which are the two slerp
       weights for each quaternion.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     quaternion1 = tf.convert_to_tensor(value=quaternion1)
     quaternion2 = tf.convert_to_tensor(value=quaternion2)
     percent = tf.convert_to_tensor(value=percent, dtype=quaternion1.dtype)
@@ -253,7 +253,7 @@ def vector_weights(vector1: type_alias.TensorLike,
     Two tensors of shape `[A1, ... , An, 1]`, representing interpolation weights
     for each input vector.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     vector1 = tf.convert_to_tensor(value=vector1)
     vector2 = tf.convert_to_tensor(value=vector2)
     percent = tf.convert_to_tensor(value=percent, dtype=vector1.dtype)