diff --git a/tensorflow_graphics/geometry/transformation/axis_angle.py b/tensorflow_graphics/geometry/transformation/axis_angle.py
index 1352312af..6418bee08 100644
--- a/tensorflow_graphics/geometry/transformation/axis_angle.py
+++ b/tensorflow_graphics/geometry/transformation/axis_angle.py
@@ -73,7 +73,7 @@ def from_euler(angles: type_alias.TensorLike,
     `[A1, ..., An, 1]`, where the first tensor represents the axis, and the
     second represents the angle. The resulting axis is a normalized vector.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     quaternion = quaternion_lib.from_euler(angles)
     return from_quaternion(quaternion)
 
@@ -109,7 +109,7 @@ def from_euler_with_small_angles_approximation(
     `[A1, ..., An, 1]`, where the first tensor represents the axis, and the
     second represents the angle. The resulting axis is a normalized vector.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     quaternion = quaternion_lib.from_euler_with_small_angles_approximation(
         angles)
     return from_quaternion(quaternion)
@@ -136,7 +136,7 @@ def from_quaternion(quaternion: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `quaternion` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     quaternion = tf.convert_to_tensor(value=quaternion)
 
     shape.check_static(
@@ -180,7 +180,7 @@ def from_rotation_matrix(rotation_matrix: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `rotation_matrix` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     rotation_matrix = tf.convert_to_tensor(value=rotation_matrix)
 
     shape.check_static(
@@ -218,7 +218,7 @@ def inverse(axis: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `axis` or `angle` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     axis = tf.convert_to_tensor(value=axis)
     angle = tf.convert_to_tensor(value=angle)
 
@@ -256,7 +256,7 @@ def is_normalized(axis: type_alias.TensorLike,
     A tensor of shape `[A1, ..., An, 1]`, where False indicates that the axis is
     not normalized.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     axis = tf.convert_to_tensor(value=axis)
     angle = tf.convert_to_tensor(value=angle)
 
@@ -305,7 +305,7 @@ def rotate(point: type_alias.TensorLike,
     ValueError: If `point`, `axis`, or `angle` are of different shape or if
     their respective shape is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     point = tf.convert_to_tensor(value=point)
     axis = tf.convert_to_tensor(value=axis)
     angle = tf.convert_to_tensor(value=angle)
diff --git a/tensorflow_graphics/geometry/transformation/dual_quaternion.py b/tensorflow_graphics/geometry/transformation/dual_quaternion.py
index 3def34a78..39dc851cc 100644
--- a/tensorflow_graphics/geometry/transformation/dual_quaternion.py
+++ b/tensorflow_graphics/geometry/transformation/dual_quaternion.py
@@ -63,7 +63,7 @@ def conjugate(dual_quaternion: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `dual_quaternion` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     dual_quaternion = tf.convert_to_tensor(value=dual_quaternion)
 
     shape.check_static(
@@ -97,7 +97,7 @@ def multiply(dual_quaternion1: type_alias.TensorLike,
   Returns:
     A tensor of shape `[A1, ..., An, 8]` representing dual quaternions.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     dual_quaternion1 = tf.convert_to_tensor(value=dual_quaternion1)
     dual_quaternion2 = tf.convert_to_tensor(value=dual_quaternion2)
 
@@ -144,7 +144,7 @@ def inverse(dual_quaternion: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `dual quaternion` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     dual_quaternion = tf.convert_to_tensor(value=dual_quaternion)
 
     shape.check_static(
@@ -193,7 +193,7 @@ def norm(dual_quaternion: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `dual quaternion` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     dual_quaternion = tf.convert_to_tensor(value=dual_quaternion)
 
     shape.check_static(
@@ -234,7 +234,7 @@ def is_normalized(dual_quaternion: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `dual_quaternion` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     dual_quaternion = tf.convert_to_tensor(value=dual_quaternion)
 
     shape.check_static(
@@ -278,7 +278,7 @@ def from_rotation_translation(
   Raises:
     ValueError: If the shape of `rotation_matrix` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     rotation_quaternion = tf.convert_to_tensor(value=rotation_quaternion)
     translation_vector = tf.convert_to_tensor(value=translation_vector)
 
@@ -319,7 +319,7 @@ def to_rotation_translation(
     A tuple with a `[A1, ..., An, 4]`-tensor for rotation in quaternion form,
     and a `[A1, ..., An, 3]`-tensor for translation, in that order.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     dual_quaternion = tf.convert_to_tensor(value=dual_quaternion)
 
     shape.check_static(
@@ -362,7 +362,7 @@ def from_axis_angle_translation(axis: type_alias.TensorLike,
     ValueError: If the shape of `axis`, `angle`, or `translation_vector`
     is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     axis = tf.convert_to_tensor(value=axis)
     angle = tf.convert_to_tensor(value=angle)
     translation_vector = tf.convert_to_tensor(value=translation_vector)
@@ -415,7 +415,7 @@ def conjugate_dual(
   Raises:
     ValueError: If the shape of `dual_quaternion` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     dual_quaternion = tf.convert_to_tensor(value=dual_quaternion)
 
     shape.check_static(
@@ -442,7 +442,7 @@ def point_to_dual_quaternion(
   Returns:
     The dual quaternion representation of `point`.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     point = tf.convert_to_tensor(value=point)
 
     shape.check_static(
diff --git a/tensorflow_graphics/geometry/transformation/euler.py b/tensorflow_graphics/geometry/transformation/euler.py
index c63bf40aa..4b406b4fd 100644
--- a/tensorflow_graphics/geometry/transformation/euler.py
+++ b/tensorflow_graphics/geometry/transformation/euler.py
@@ -60,7 +60,7 @@ def from_axis_angle(axis: type_alias.TensorLike,
     A tensor of shape `[A1, ..., An, 3]`, where the last dimension represents
     the three Euler angles.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     return from_quaternion(quaternion.from_axis_angle(axis, angle))
 
 
@@ -98,7 +98,7 @@ def gimbal_lock(r01, r02, r20, eps_addition):
     angles = tf.stack((theta_x, theta_y, theta_z), axis=-1)
     return angles
 
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     quaternions = tf.convert_to_tensor(value=quaternions)
 
     shape.check_static(
@@ -193,7 +193,7 @@ def gimbal_lock(rotation_matrix, r20, eps_addition):
     angles = tf.stack((theta_x, theta_y, theta_z), axis=-1)
     return angles
 
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     rotation_matrix = tf.convert_to_tensor(value=rotation_matrix)
 
     shape.check_static(
@@ -232,7 +232,7 @@ def inverse(euler_angle: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `euler_angle` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     euler_angle = tf.convert_to_tensor(value=euler_angle)
 
     shape.check_static(
diff --git a/tensorflow_graphics/geometry/transformation/linear_blend_skinning.py b/tensorflow_graphics/geometry/transformation/linear_blend_skinning.py
index 8ceb3e91b..58870a6d7 100644
--- a/tensorflow_graphics/geometry/transformation/linear_blend_skinning.py
+++ b/tensorflow_graphics/geometry/transformation/linear_blend_skinning.py
@@ -56,7 +56,7 @@ def blend(points: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of the input tensors are not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     points = tf.convert_to_tensor(value=points)
     skinning_weights = tf.convert_to_tensor(value=skinning_weights)
     bone_rotations = tf.convert_to_tensor(value=bone_rotations)
diff --git a/tensorflow_graphics/geometry/transformation/look_at.py b/tensorflow_graphics/geometry/transformation/look_at.py
index 42d8fa526..91f7267ed 100644
--- a/tensorflow_graphics/geometry/transformation/look_at.py
+++ b/tensorflow_graphics/geometry/transformation/look_at.py
@@ -50,7 +50,7 @@ def right_handed(camera_position: type_alias.TensorLike,
     A tensor of shape `[A1, ..., An, 4, 4]`, containing right handed look at
     matrices.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     camera_position = tf.convert_to_tensor(value=camera_position)
     look_at = tf.convert_to_tensor(value=look_at)
     up_vector = tf.convert_to_tensor(value=up_vector)
diff --git a/tensorflow_graphics/geometry/transformation/quaternion.py b/tensorflow_graphics/geometry/transformation/quaternion.py
index e7fa4f3ea..abb02c83d 100644
--- a/tensorflow_graphics/geometry/transformation/quaternion.py
+++ b/tensorflow_graphics/geometry/transformation/quaternion.py
@@ -98,7 +98,7 @@ def between_two_vectors_3d(vector1: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `vector1` or `vector2` is not supported.
   """
-  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)
 
@@ -154,7 +154,7 @@ def conjugate(quaternion: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `quaternion` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     quaternion = tf.convert_to_tensor(value=quaternion)
 
     shape.check_static(
@@ -187,7 +187,7 @@ def from_axis_angle(axis: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `axis` or `angle` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     axis = tf.convert_to_tensor(value=axis)
     angle = tf.convert_to_tensor(value=angle)
 
@@ -229,7 +229,7 @@ def from_euler(angles: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `angles` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     angles = tf.convert_to_tensor(value=angles)
 
     shape.check_static(
@@ -272,7 +272,7 @@ def from_euler_with_small_angles_approximation(
   Raises:
     ValueError: If the shape of `angles` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     angles = tf.convert_to_tensor(value=angles)
 
     shape.check_static(
@@ -310,7 +310,7 @@ def from_rotation_matrix(rotation_matrix: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `rotation_matrix` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     rotation_matrix = tf.convert_to_tensor(value=rotation_matrix)
 
     shape.check_static(
@@ -395,7 +395,7 @@ def inverse(quaternion: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `quaternion` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     quaternion = tf.convert_to_tensor(value=quaternion)
 
     shape.check_static(
@@ -429,7 +429,7 @@ def is_normalized(quaternion: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `quaternion` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     quaternion = tf.convert_to_tensor(value=quaternion)
 
     shape.check_static(
@@ -463,7 +463,7 @@ def normalize(quaternion: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `quaternion` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     quaternion = tf.convert_to_tensor(value=quaternion)
 
     shape.check_static(
@@ -494,7 +494,7 @@ def multiply(quaternion1: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `quaternion1` or `quaternion2` is not supported.
   """
-  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)
 
@@ -526,7 +526,7 @@ def normalized_random_uniform(quaternion_shape: List[int],
     A tensor of shape `[quaternion_shape[0],...,quaternion_shape[-1], 4]`
     representing random normalized quaternions.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     quaternion_shape = tf.convert_to_tensor(
         value=quaternion_shape, dtype=tf.int32)
     quaternion_shape = tf.concat((quaternion_shape, tf.constant([4])), axis=0)
@@ -594,7 +594,7 @@ def rotate(point: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `point` or `quaternion` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     point = tf.convert_to_tensor(value=point)
     quaternion = tf.convert_to_tensor(value=quaternion)
 
@@ -642,7 +642,7 @@ def relative_angle(quaternion1: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `quaternion1` or `quaternion2` is not supported.
   """
-  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)
 
diff --git a/tensorflow_graphics/geometry/transformation/rotation_matrix_2d.py b/tensorflow_graphics/geometry/transformation/rotation_matrix_2d.py
index 6a0aa37a3..e572427eb 100644
--- a/tensorflow_graphics/geometry/transformation/rotation_matrix_2d.py
+++ b/tensorflow_graphics/geometry/transformation/rotation_matrix_2d.py
@@ -77,7 +77,7 @@ def from_euler(angle: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `angle` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     angle = tf.convert_to_tensor(value=angle)
 
     shape.check_static(
@@ -132,7 +132,7 @@ def from_euler_with_small_angles_approximation(
   Raises:
     ValueError: If the shape of `angle` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     angles = tf.convert_to_tensor(value=angles)
 
     shape.check_static(
@@ -166,7 +166,7 @@ def inverse(matrix: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `matrix` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     matrix = tf.convert_to_tensor(value=matrix)
 
     shape.check_static(
@@ -201,7 +201,7 @@ def is_valid(matrix: type_alias.TensorLike,
     A tensor of type `bool` and shape `[A1, ..., An, 1]` where False indicates
     that the input is not a valid rotation matrix.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     matrix = tf.convert_to_tensor(value=matrix)
 
     shape.check_static(
@@ -236,7 +236,7 @@ def rotate(point: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `point` or `matrix` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     point = tf.convert_to_tensor(value=point)
     matrix = tf.convert_to_tensor(value=matrix)
 
diff --git a/tensorflow_graphics/geometry/transformation/rotation_matrix_3d.py b/tensorflow_graphics/geometry/transformation/rotation_matrix_3d.py
index 50908ca67..8cb9dfb8a 100644
--- a/tensorflow_graphics/geometry/transformation/rotation_matrix_3d.py
+++ b/tensorflow_graphics/geometry/transformation/rotation_matrix_3d.py
@@ -99,7 +99,7 @@ def assert_rotation_matrix_normalized(
   if not FLAGS[tfg_flags.TFG_ADD_ASSERTS_TO_GRAPH].value:
     return matrix
 
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     matrix = tf.convert_to_tensor(value=matrix)
 
     shape.check_static(
@@ -142,7 +142,7 @@ def from_axis_angle(
   Raises:
     ValueError: If the shape of `axis` or `angle` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     axis = tf.convert_to_tensor(value=axis)
     angle = tf.convert_to_tensor(value=angle)
 
@@ -204,7 +204,7 @@ def from_euler(angles: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `angles` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     angles = tf.convert_to_tensor(value=angles)
 
     shape.check_static(
@@ -244,7 +244,7 @@ def from_euler_with_small_angles_approximation(
   Raises:
     ValueError: If the shape of `angles` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     angles = tf.convert_to_tensor(value=angles)
 
     shape.check_static(
@@ -276,7 +276,7 @@ def from_quaternion(
   Raises:
     ValueError: If the shape of `quaternion` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     quaternion = tf.convert_to_tensor(value=quaternion)
 
     shape.check_static(
@@ -323,7 +323,7 @@ def inverse(matrix: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of `matrix` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     matrix = tf.convert_to_tensor(value=matrix)
 
     shape.check_static(
@@ -356,7 +356,7 @@ def is_valid(matrix: type_alias.TensorLike,
     A tensor of type `bool` and shape `[A1, ..., An, 1]` where False indicates
     that the input is not a valid rotation matrix.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     matrix = tf.convert_to_tensor(value=matrix)
 
     shape.check_static(
@@ -392,7 +392,7 @@ def rotate(point: type_alias.TensorLike,
     ValueError: If the shape of `point` or `rotation_matrix_3d` is not
     supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     point = tf.convert_to_tensor(value=point)
     matrix = tf.convert_to_tensor(value=matrix)
 
diff --git a/tensorflow_graphics/geometry/transformation/rotation_matrix_common.py b/tensorflow_graphics/geometry/transformation/rotation_matrix_common.py
index 185d58677..41998bb1c 100644
--- a/tensorflow_graphics/geometry/transformation/rotation_matrix_common.py
+++ b/tensorflow_graphics/geometry/transformation/rotation_matrix_common.py
@@ -45,7 +45,7 @@ def is_valid(matrix: type_alias.TensorLike,
     A tensor of type `bool` and shape `[A1, ..., An, 1]` where False indicates
     that the input is not a valid rotation matrix.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     matrix = tf.convert_to_tensor(value=matrix)
 
     shape.check_static(
diff --git a/tensorflow_graphics/math/feature_representation.py b/tensorflow_graphics/math/feature_representation.py
index 35b9cf858..a0cda9b00 100644
--- a/tensorflow_graphics/math/feature_representation.py
+++ b/tensorflow_graphics/math/feature_representation.py
@@ -37,7 +37,7 @@ def positional_encoding(features: TensorLike,
   Returns:
     A tensor of shape `[A1, ..., An, 2*N*M + M]`.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     features = tf.convert_to_tensor(value=features)
 
     output = [features]
diff --git a/tensorflow_graphics/math/math_helpers.py b/tensorflow_graphics/math/math_helpers.py
index a58e2d913..9976511f4 100644
--- a/tensorflow_graphics/math/math_helpers.py
+++ b/tensorflow_graphics/math/math_helpers.py
@@ -52,7 +52,7 @@ def cartesian_to_spherical_coordinates(
     (`r`,`theta`,`phi`), where `r` is the sphere radius, `theta` is the polar
     angle and `phi` is the azimuthal angle. Returns `NaN` gradient if x = y = 0.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     point_cartesian = tf.convert_to_tensor(value=point_cartesian)
 
     shape.check_static(
@@ -140,7 +140,7 @@ def spherical_to_cartesian_coordinates(
     A tensor of shape `[A1, ..., An, 3]`, where the last dimension contains the
     cartesian coordinates in x,y,z order.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     point_spherical = tf.convert_to_tensor(value=point_spherical)
 
     shape.check_static(
@@ -180,7 +180,7 @@ def square_to_spherical_coordinates(
     [0,1].
 
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     point_2d = tf.convert_to_tensor(value=point_2d)
 
     shape.check_static(
diff --git a/tensorflow_graphics/math/sampling.py b/tensorflow_graphics/math/sampling.py
index 0a0a067c0..e89553f00 100644
--- a/tensorflow_graphics/math/sampling.py
+++ b/tensorflow_graphics/math/sampling.py
@@ -40,7 +40,7 @@ def regular_1d(near: TensorLike,
   Returns:
     A tensor of shape `[A1, ..., An, M]` indicating the M points on the ray
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     near = tf.convert_to_tensor(near)
     far = tf.convert_to_tensor(far)
 
@@ -70,7 +70,7 @@ def regular_inverse_1d(near: TensorLike,
   Returns:
     A tensor of shape `[A1, ..., An, M]` indicating the M points on the ray
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     near = tf.convert_to_tensor(near)
     far = tf.convert_to_tensor(far)
 
@@ -100,7 +100,7 @@ def uniform_1d(near: TensorLike,
   Returns:
     A tensor of shape `[A1, ..., An, M]` indicating the M points on the ray
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     near = tf.convert_to_tensor(near)
     far = tf.convert_to_tensor(far)
 
@@ -135,7 +135,7 @@ def stratified_1d(near: TensorLike,
   Returns:
     A tensor of shape `[A1, ..., An, M]` indicating the M points on the ray
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     near = tf.convert_to_tensor(near)
     far = tf.convert_to_tensor(far)
 
@@ -175,7 +175,7 @@ def logspace_1d(near: TensorLike,
   Returns:
     A tensor of shape `[A1, ..., An, M]` indicating the M points on the ray
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     near = tf.convert_to_tensor(near)
     far = tf.convert_to_tensor(far)
     shape.compare_batch_dimensions(
@@ -209,7 +209,7 @@ def geomspace_1d(near: TensorLike,
   Returns:
     A tensor of shape `[A1, ..., An, M]` indicating the M points on the ray
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     near = tf.convert_to_tensor(near)
     far = tf.convert_to_tensor(far)
     shape.compare_batch_dimensions(
@@ -238,7 +238,7 @@ def stratified_geomspace_1d(near: TensorLike,
   Returns:
     A tensor of shape `[A1, ..., An, M]` indicating the M points on the ray
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     near = tf.convert_to_tensor(near)
     far = tf.convert_to_tensor(far)
 
@@ -268,7 +268,7 @@ def _normalize_pdf(pdf: TensorLike, name="normalize_pdf") -> tf.Tensor:
   Returns:
     A tensor of shape `[A1, ..., An, M]`.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     pdf = tf.convert_to_tensor(value=pdf)
     pdf += 1e-5
     return safe_ops.safe_signed_div(pdf, tf.reduce_sum(pdf, -1, keepdims=True))
@@ -285,7 +285,7 @@ def _get_cdf(pdf: TensorLike, name="get_cdf"):
   Returns:
     A tensor of shape `[A1, ..., An, M+1]`.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     pdf = tf.convert_to_tensor(value=pdf)
     batch_shape = tf.shape(pdf)[:-1]
     cdf = tf.cumsum(pdf, -1)
@@ -317,7 +317,7 @@ def inverse_transform_sampling_1d(
     A tensor of shape `[A1, ..., An, N]` indicating the new N random points.
   """
 
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     bins = tf.convert_to_tensor(value=bins)
     pdf = tf.convert_to_tensor(value=pdf)
 
@@ -378,7 +378,7 @@ def inverse_transform_stratified_1d(bin_start: TensorLike,
     A tensor of shape `[A1, ..., An, N]` indicating the N points on the ray
   """
 
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     bin_start = tf.convert_to_tensor(value=bin_start)
     bin_width = tf.convert_to_tensor(value=bin_width)
     pdf = tf.convert_to_tensor(value=pdf)
diff --git a/tensorflow_graphics/math/spherical_harmonics.py b/tensorflow_graphics/math/spherical_harmonics.py
index 19fd1a74b..39db7601b 100644
--- a/tensorflow_graphics/math/spherical_harmonics.py
+++ b/tensorflow_graphics/math/spherical_harmonics.py
@@ -58,7 +58,7 @@ def integration_product(
     ValueError: if the last dimension of `harmonics1` is different from the last
     dimension of `harmonics2`.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     harmonics1 = tf.convert_to_tensor(value=harmonics1)
     harmonics2 = tf.convert_to_tensor(value=harmonics2)
 
@@ -88,7 +88,7 @@ def generate_l_m_permutations(
   Returns:
     Two tensors of shape `[max_band*max_band]`.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     degree_l = []
     order_m = []
     for degree in range(0, max_band + 1):
@@ -112,7 +112,7 @@ def generate_l_m_zonal(
   Returns:
     Two tensors of shape `[max_band+1]`, one for degree l and one for order m.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     degree_l = np.linspace(0, max_band, num=max_band + 1, dtype=np.int32)
     order_m = np.zeros(max_band + 1, dtype=np.int32)
     return (tf.convert_to_tensor(value=degree_l),
@@ -273,7 +273,7 @@ def evaluate_spherical_harmonics(
     InvalidArgumentError: if at least an element of `l`, `m`, `theta` or `phi`
     is outside the expected range.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     degree_l = tf.convert_to_tensor(value=degree_l)
     order_m = tf.convert_to_tensor(value=order_m)
     theta = tf.convert_to_tensor(value=theta)
@@ -339,7 +339,7 @@ def rotate_zonal_harmonics(
     ValueError: If the shape of `zonal_coeffs`, `theta` or `phi` is not
       supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     zonal_coeffs = tf.convert_to_tensor(value=zonal_coeffs)
     theta = tf.convert_to_tensor(value=theta)
     phi = tf.convert_to_tensor(value=phi)
@@ -386,7 +386,7 @@ def tile_zonal_coefficients(
   Raises:
     ValueError: if the shape of `coefficients` is not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     coefficients = tf.convert_to_tensor(value=coefficients)
 
     shape.check_static(
diff --git a/tensorflow_graphics/math/vector.py b/tensorflow_graphics/math/vector.py
index d59656045..c275b9b2e 100644
--- a/tensorflow_graphics/math/vector.py
+++ b/tensorflow_graphics/math/vector.py
@@ -47,7 +47,7 @@ def cross(vector1: TensorLike,
     A tensor of shape `[A1, ..., Ai = 3, ..., An]`, where the dimension i = axis
     represents the result of the cross product.
   """
-  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)
 
@@ -90,7 +90,7 @@ def dot(vector1: TensorLike,
     A tensor of shape `[A1, ..., Ai = 1, ..., An]`, where the dimension i = axis
     represents the result of the dot product.
   """
-  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)
 
@@ -132,7 +132,7 @@ def reflect(vector: TensorLike,
     A tensor of shape `[A1, ..., Ai, ..., An]`, where the dimension i = axis
     represents a reflected vector.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     vector = tf.convert_to_tensor(value=vector)
     normal = tf.convert_to_tensor(value=normal)
 
diff --git a/tensorflow_graphics/projects/gan/architectures_progressive_gan.py b/tensorflow_graphics/projects/gan/architectures_progressive_gan.py
index 217025f34..85c73984f 100644
--- a/tensorflow_graphics/projects/gan/architectures_progressive_gan.py
+++ b/tensorflow_graphics/projects/gan/architectures_progressive_gan.py
@@ -103,7 +103,7 @@ def create_generator(latent_code_dimension: int = 128,
      The created generator keras model object.
   """
   if kernel_initializer is None:
-    kernel_initializer = tf.keras.initializers.TruncatedNormal(  # pyrefly: ignore[bad-assignment]
+    kernel_initializer = tf.keras.initializers.TruncatedNormal(
         mean=0.0, stddev=1.0)
 
   input_tensor = tf.keras.Input(shape=(latent_code_dimension,))
@@ -118,9 +118,9 @@ def create_generator(latent_code_dimension: int = 128,
       units=4 * 4 * latent_code_dimension,
       kernel_initializer=kernel_initializer)(
           maybe_normzlized_input_tensor)
-  tensor = tf.keras.layers.Reshape(target_shape=(4, 4, latent_code_dimension))(  # pyrefly: ignore[not-callable]
+  tensor = tf.keras.layers.Reshape(target_shape=(4, 4, latent_code_dimension))(
       tensor)
-  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
   if use_batch_normalization:
     tensor = tf.keras.layers.BatchNormalization()(tensor)
   if use_pixel_normalization:
@@ -132,7 +132,7 @@ def create_generator(latent_code_dimension: int = 128,
       padding='same',
       kernel_initializer=kernel_initializer)(
           tensor)
-  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
   if use_batch_normalization:
     tensor = tf.keras.layers.BatchNormalization()(tensor)
   if use_pixel_normalization:
@@ -144,7 +144,7 @@ def create_generator(latent_code_dimension: int = 128,
       outputs.append(
           to_rgb(
               input_tensor=tensor,
-              kernel_initializer=kernel_initializer,  # pyrefly: ignore[bad-argument-type]
+              kernel_initializer=kernel_initializer,
               name='side_output_%d_conv' % index))
     tensor = keras_layers.TwoByTwoNearestNeighborUpSampling()(tensor)
 
@@ -156,7 +156,7 @@ def create_generator(latent_code_dimension: int = 128,
           padding='same',
           kernel_initializer=kernel_initializer)(
               tensor)
-      tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+      tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
       if use_batch_normalization:
         tensor = tf.keras.layers.BatchNormalization()(tensor)
       if use_pixel_normalization:
@@ -164,7 +164,7 @@ def create_generator(latent_code_dimension: int = 128,
 
   tensor = to_rgb(
       input_tensor=tensor,
-      kernel_initializer=kernel_initializer,  # pyrefly: ignore[bad-argument-type]
+      kernel_initializer=kernel_initializer,
       name='final_output')
   if generate_intermediate_outputs:
     outputs.append(tensor)
@@ -213,7 +213,7 @@ def from_rgb(input_tensor: tf.Tensor,
   Returns:
     The feature map.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     output = create_conv_layer(
         use_fan_in_scaled_kernel=use_fan_in_scaled_kernel,
         filters=num_channels,
@@ -222,7 +222,7 @@ def from_rgb(input_tensor: tf.Tensor,
         kernel_initializer=kernel_initializer,
         padding='same')(
             input_tensor)
-    return tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(output)  # pyrefly: ignore[not-callable]
+    return tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(output)
 
 
 def create_discriminator(
@@ -270,7 +270,7 @@ def create_discriminator(
     The generated discriminator keras model.
   """
   if kernel_initializer is None:
-    kernel_initializer = tf.keras.initializers.TruncatedNormal(  # pyrefly: ignore[bad-assignment]
+    kernel_initializer = tf.keras.initializers.TruncatedNormal(
         mean=0.0, stddev=1.0)
 
   if use_intermediate_inputs:
@@ -286,7 +286,7 @@ def create_discriminator(
       tensor,
       use_fan_in_scaled_kernel=use_fan_in_scaled_kernels,
       num_channels=downsampling_blocks_num_channels[0][0],
-      kernel_initializer=kernel_initializer,  # pyrefly: ignore[bad-argument-type]
+      kernel_initializer=kernel_initializer,
       relu_leakiness=relu_leakiness)
   if use_layer_normalization:
     tensor = tfa_normalizations.GroupNormalization(groups=1)(tensor)
@@ -301,7 +301,7 @@ def create_discriminator(
         padding='same',
         kernel_initializer=kernel_initializer)(
             tensor)
-    tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+    tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
     if use_layer_normalization:
       tensor = tfa_normalizations.GroupNormalization(groups=1)(tensor)
     tensor = create_conv_layer(
@@ -312,7 +312,7 @@ def create_discriminator(
         padding='same',
         kernel_initializer=kernel_initializer)(
             tensor)
-    tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+    tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
     if use_layer_normalization:
       tensor = tfa_normalizations.GroupNormalization(groups=1)(tensor)
     if use_antialiased_bilinear_downsampling:
@@ -330,7 +330,7 @@ def create_discriminator(
       padding='same',
       kernel_initializer=kernel_initializer)(
           tensor)
-  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
   if use_layer_normalization:
     tensor = tfa_normalizations.GroupNormalization(groups=1)(tensor)
 
@@ -342,7 +342,7 @@ def create_discriminator(
       padding='valid',
       kernel_initializer=kernel_initializer)(
           tensor)
-  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
   if use_layer_normalization:
     tensor = tfa_normalizations.GroupNormalization(groups=1)(tensor)
 
@@ -353,7 +353,7 @@ def create_discriminator(
       kernel_size=1,
       kernel_initializer=kernel_initializer)(
           tensor)
-  tensor = tf.keras.layers.Reshape((-1,))(tensor)  # pyrefly: ignore[not-callable]
+  tensor = tf.keras.layers.Reshape((-1,))(tensor)
 
   if use_intermediate_inputs:
     return tf.keras.Model(inputs=inputs, outputs=tensor, name=name)  # pyrefly: ignore[unbound-name]
diff --git a/tensorflow_graphics/projects/gan/architectures_style_gan.py b/tensorflow_graphics/projects/gan/architectures_style_gan.py
index a6a53ac79..81c8d9627 100644
--- a/tensorflow_graphics/projects/gan/architectures_style_gan.py
+++ b/tensorflow_graphics/projects/gan/architectures_style_gan.py
@@ -113,7 +113,7 @@ def create_mapping_network(latent_code_dimension: int = 128,
 
   tensor = maybe_normalized_input_tensor
   for i in range(num_layers):
-    with tf.name_scope(name='mapping_layer_%d' % i):  # pyrefly: ignore[bad-instantiation]
+    with tf.name_scope(name='mapping_layer_%d' % i):
       # The kernel_multiplier implements the reduced learning rate of the
       # mapping network.
       tensor = keras_layers.FanInScaledDense(
@@ -123,7 +123,7 @@ def create_mapping_network(latent_code_dimension: int = 128,
           kernel_initializer=tf.keras.initializers.TruncatedNormal(
               mean=0.0, stddev=1.0 / learning_rate_multiplier))(
                   tensor)
-      tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+      tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
   return tf.keras.Model(inputs=input_tensor, outputs=tensor, name=name)
 
 
@@ -162,7 +162,7 @@ def create_synthesis_network(latent_code_dimension: int = 128,
 
   tensor = keras_layers.LearnedConstant()(mapped_latent_code_input)
   tensor = keras_layers.Noise()(tensor)
-  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
   tensor = apply_style_with_adain(
       mapped_latent_code=mapped_latent_code_input, input_tensor=tensor)
   tensor = keras_layers.FanInScaledConv2D(
@@ -173,7 +173,7 @@ def create_synthesis_network(latent_code_dimension: int = 128,
       kernel_initializer=kernel_initializer)(
           tensor)
   tensor = keras_layers.Noise()(tensor)
-  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
   tensor = apply_style_with_adain(
       mapped_latent_code=mapped_latent_code_input, input_tensor=tensor)
 
@@ -197,7 +197,7 @@ def create_synthesis_network(latent_code_dimension: int = 128,
           kernel_initializer=kernel_initializer)(
               tensor)
       tensor = keras_layers.Noise()(tensor)
-      tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+      tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
       tensor = apply_style_with_adain(
           mapped_latent_code=mapped_latent_code_input, input_tensor=tensor)
 
@@ -262,8 +262,8 @@ def create_style_based_generator(
       use_bilinear_upsampling=use_bilinear_upsampling)
 
   input_tensor = tf.keras.Input(shape=(latent_code_dimension,))
-  mapped_latent_code = mapping_network(input_tensor)  # pyrefly: ignore[not-callable]
-  generated_images = synthesis_network(mapped_latent_code)  # pyrefly: ignore[not-callable]
+  mapped_latent_code = mapping_network(input_tensor)
+  generated_images = synthesis_network(mapped_latent_code)
   generator = tf.keras.Model(
       inputs=input_tensor, outputs=generated_images, name=name)
 
diff --git a/tensorflow_graphics/projects/gan/architectures_style_gan_v2.py b/tensorflow_graphics/projects/gan/architectures_style_gan_v2.py
index 64a2735df..4a97e4067 100644
--- a/tensorflow_graphics/projects/gan/architectures_style_gan_v2.py
+++ b/tensorflow_graphics/projects/gan/architectures_style_gan_v2.py
@@ -135,7 +135,7 @@ def create_synthesis_network(
     tensor = keras_layers.Noise()((tensor, noise_inputs[0]))  # pyrefly: ignore[unbound-name]
   else:
     tensor = keras_layers.Noise()(tensor)
-  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
 
   output = None
   for index, channels in enumerate(upsampling_blocks_num_channels):
@@ -157,7 +157,7 @@ def create_synthesis_network(
         tensor = keras_layers.Noise()((tensor, noise_inputs[noise_index]))
       else:
         tensor = keras_layers.Noise()(tensor)
-      tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+      tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
 
   output = _maybe_upsample_and_add_outputs(
       architectures_progressive_gan.to_rgb(
@@ -270,7 +270,7 @@ def create_discriminator(
     The generated discriminator keras model.
   """
   if kernel_initializer is None:
-    kernel_initializer = tf.keras.initializers.TruncatedNormal(  # pyrefly: ignore[bad-assignment]
+    kernel_initializer = tf.keras.initializers.TruncatedNormal(
         mean=0.0, stddev=1.0)
 
   input_tensor = tf.keras.Input(shape=(None, None, num_channels))
@@ -283,7 +283,7 @@ def create_discriminator(
 
   for index, (channels_1,
               channels_2) in enumerate(downsampling_blocks_num_channels):
-    with tf.name_scope(f'downsampling_block_{index}'):  # pyrefly: ignore[bad-instantiation]
+    with tf.name_scope(f'downsampling_block_{index}'):
       shortcut = tensor
 
       shortcut = architectures_progressive_gan.create_conv_layer(
@@ -306,7 +306,7 @@ def create_discriminator(
           padding='same',
           kernel_initializer=kernel_initializer)(
               tensor)
-      tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+      tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
       tensor = architectures_progressive_gan.create_conv_layer(
           use_fan_in_scaled_kernel=use_fan_in_scaled_kernels,
           filters=channels_2,
@@ -315,7 +315,7 @@ def create_discriminator(
           padding='same',
           kernel_initializer=kernel_initializer)(
               tensor)
-      tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+      tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
       if use_antialiased_bilinear_downsampling:
         tensor = keras_layers.Blur2D()(tensor)
       tensor = tf.keras.layers.AveragePooling2D()(tensor)
@@ -331,7 +331,7 @@ def create_discriminator(
       padding='same',
       kernel_initializer=kernel_initializer)(
           tensor)
-  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
 
   tensor = architectures_progressive_gan.create_conv_layer(
       use_fan_in_scaled_kernel=use_fan_in_scaled_kernels,
@@ -341,7 +341,7 @@ def create_discriminator(
       padding='valid',
       kernel_initializer=kernel_initializer)(
           tensor)
-  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)  # pyrefly: ignore[not-callable]
+  tensor = tf.keras.layers.LeakyReLU(alpha=relu_leakiness)(tensor)
 
   tensor = architectures_progressive_gan.create_conv_layer(
       use_fan_in_scaled_kernel=use_fan_in_scaled_kernels,
@@ -350,6 +350,6 @@ def create_discriminator(
       kernel_size=1,
       kernel_initializer=kernel_initializer)(
           tensor)
-  tensor = tf.keras.layers.Reshape((-1,))(tensor)  # pyrefly: ignore[not-callable]
+  tensor = tf.keras.layers.Reshape((-1,))(tensor)
 
   return tf.keras.Model(inputs=input_tensor, outputs=tensor, name=name)
diff --git a/tensorflow_graphics/projects/gan/exponential_moving_average.py b/tensorflow_graphics/projects/gan/exponential_moving_average.py
index fc8327807..0a662ca75 100644
--- a/tensorflow_graphics/projects/gan/exponential_moving_average.py
+++ b/tensorflow_graphics/projects/gan/exponential_moving_average.py
@@ -51,7 +51,7 @@ def __init__(self, decay: float = 0.999):
 
   def _ema_assign_fn(self, variable: tf.Variable, value: tf.Tensor):
     """Updates the exponential moving average for a single variable."""
-    return variable.assign(self._decay * variable + (1.0 - self._decay) * value)  # pyrefly: ignore[unsupported-operation]
+    return variable.assign(self._decay * variable + (1.0 - self._decay) * value)
 
   def _apply_values(self, variables: Sequence[tf.Variable]):
     """Applies the new values to the exponential moving averages."""
diff --git a/tensorflow_graphics/projects/gan/keras_layers.py b/tensorflow_graphics/projects/gan/keras_layers.py
index ee7630a64..e143cda95 100644
--- a/tensorflow_graphics/projects/gan/keras_layers.py
+++ b/tensorflow_graphics/projects/gan/keras_layers.py
@@ -88,7 +88,7 @@ def kernel(self) -> tf.Tensor:
     # Fan in computation assumes that the last dimension of the kernel is the
     # output dimension.
     fan_in = np.prod(self._kernel.shape.as_list()[:-1])
-    return (self._kernel * self._multiplier / math.sqrt(fan_in) *  # pyrefly: ignore[bad-return, unsupported-operation]
+    return (self._kernel * self._multiplier / math.sqrt(fan_in) *
             self._kernel_multiplier)
 
   @kernel.setter
@@ -98,7 +98,7 @@ def kernel(self, kernel: tf.Variable) -> None:
   @property
   def bias(self) -> tf.Tensor:
     if self._bias_multiplier is not None:
-      return self._bias * self._bias_multiplier  # pyrefly: ignore[bad-return, unsupported-operation]
+      return self._bias * self._bias_multiplier
     else:
       return self._bias.read_value()
 
@@ -384,7 +384,7 @@ def call(
       raise ValueError('inputs needs to have two elements.')
 
     feature_map, mapped_latent_code = inputs
-    style = self._dense_layer(mapped_latent_code)[:, tf.newaxis, tf.newaxis, :]  # pyrefly: ignore[not-callable]
+    style = self._dense_layer(mapped_latent_code)[:, tf.newaxis, tf.newaxis, :]
 
     modulated_features = self._conv_layer(feature_map * style)
     weight = self._conv_layer.kernel[tf.newaxis, :, :, :, :]
diff --git a/tensorflow_graphics/projects/gan/losses.py b/tensorflow_graphics/projects/gan/losses.py
index 28808c162..ff518414c 100644
--- a/tensorflow_graphics/projects/gan/losses.py
+++ b/tensorflow_graphics/projects/gan/losses.py
@@ -54,7 +54,7 @@ def gradient_penalty_loss(real_data: Union[tf.Tensor, Sequence[tf.Tensor]],
     ValueError if the numnber of elements in real_data and generated_data are
     not equal.
   """
-  with tf.name_scope(name=name_scope):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name=name_scope):
     with tf.GradientTape() as tape:
       if (isinstance(real_data, tf.Tensor) and
           isinstance(generated_data, tf.Tensor)):
@@ -88,9 +88,9 @@ def gradient_penalty_loss(real_data: Union[tf.Tensor, Sequence[tf.Tensor]],
             (type(real_data), type(generated_data)))
       # By default the gradient tape only watches trainable variables.
       tape.watch(interpolated_data)
-      interpolated_labels = discriminator(interpolated_data)  # pyrefly: ignore[not-callable]
+      interpolated_labels = discriminator(interpolated_data)
 
-      with tf.name_scope(name='gradients'):  # pyrefly: ignore[bad-instantiation]
+      with tf.name_scope(name='gradients'):
         gradients = tape.gradient(
             target=interpolated_labels, sources=interpolated_data)
 
@@ -146,12 +146,12 @@ def r1_regularization(real_data: Union[tf.Tensor, Sequence[tf.Tensor]],
   Returns:
     The r1 regulatization loss per example as tensor of shape [batch_size].
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     with tf.GradientTape() as tape:
       tape.watch(real_data)
-      discriminator_output = discriminator(real_data)  # pyrefly: ignore[not-callable]
+      discriminator_output = discriminator(real_data)
 
-      with tf.name_scope(name='gradients'):  # pyrefly: ignore[bad-instantiation]
+      with tf.name_scope(name='gradients'):
         gradients = tape.gradient(
             target=discriminator_output, sources=real_data)
 
@@ -186,7 +186,7 @@ def wasserstein_generator_loss(
   Returns:
     The loss for the generator.
   """
-  with tf.name_scope(name=name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name=name):
     return -discriminator_output_generated_data
 
 
@@ -211,8 +211,8 @@ def wasserstein_discriminator_loss(
   Returns:
     The loss for the discriminator.
   """
-  with tf.name_scope(name=name):  # pyrefly: ignore[bad-instantiation]
-    return discriminator_output_generated_data - discriminator_output_real_data  # pyrefly: ignore[unsupported-operation]
+  with tf.name_scope(name=name):
+    return discriminator_output_generated_data - discriminator_output_real_data
 
 
 def wasserstein_hinge_generator_loss(
@@ -235,7 +235,7 @@ def wasserstein_hinge_generator_loss(
   Returns:
     The loss for the generator.
   """
-  with tf.name_scope(name=name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name=name):
     return -discriminator_output_generated_data
 
 
@@ -260,9 +260,9 @@ def wasserstein_hinge_discriminator_loss(
   Returns:
     The loss for the discriminator.
   """
-  with tf.name_scope(name=name):  # pyrefly: ignore[bad-instantiation]
-    return tf.nn.relu(1.0 - discriminator_output_real_data) + tf.nn.relu(  # pyrefly: ignore[unsupported-operation]
-        discriminator_output_generated_data + 1.0)  # pyrefly: ignore[unsupported-operation]
+  with tf.name_scope(name=name):
+    return tf.nn.relu(1.0 - discriminator_output_real_data) + tf.nn.relu(
+        discriminator_output_generated_data + 1.0)
 
 
 def minimax_generator_loss(discriminator_output_generated_data: tf.Tensor,
@@ -280,7 +280,7 @@ def minimax_generator_loss(discriminator_output_generated_data: tf.Tensor,
   Returns:
     The loss for the generator.
   """
-  with tf.name_scope(name=name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name=name):
     # -log(sigmoid(discriminator_output_generated_data))
     return tf.nn.sigmoid_cross_entropy_with_logits(
         labels=tf.ones_like(discriminator_output_generated_data),
@@ -306,7 +306,7 @@ def minimax_discriminator_loss(
   Returns:
     The loss for the discriminator.
   """
-  with tf.name_scope(name=name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name=name):
     # -log(sigmoid(discriminator_output_real_data))
     loss_real = tf.nn.sigmoid_cross_entropy_with_logits(
         labels=tf.ones_like(discriminator_output_real_data),
diff --git a/tensorflow_graphics/projects/gan/minibatch_standarddeviation.py b/tensorflow_graphics/projects/gan/minibatch_standarddeviation.py
index 5317b2e0d..da3336487 100644
--- a/tensorflow_graphics/projects/gan/minibatch_standarddeviation.py
+++ b/tensorflow_graphics/projects/gan/minibatch_standarddeviation.py
@@ -141,7 +141,7 @@ class SyncMiniBatchStandardDeviation(MiniBatchStandardDeviationBase):
 
   def _calculate_mean_feature_standard_deviation(self, inputs, batch_axis):
 
-    with tf.name_scope('mean_feature_standard_deviation'):  # pyrefly: ignore[bad-instantiation]
+    with tf.name_scope('mean_feature_standard_deviation'):
       # The dynamic range of fp16 is too limited to support the collection of
       # sufficient statistics. As a workaround we simply perform the operations
       # on 32-bit floats before converting the mean and variance back to fp16
diff --git a/tensorflow_graphics/rendering/volumetric/absorption.py b/tensorflow_graphics/rendering/volumetric/absorption.py
index cc9bf4a62..32a53b56e 100644
--- a/tensorflow_graphics/rendering/volumetric/absorption.py
+++ b/tensorflow_graphics/rendering/volumetric/absorption.py
@@ -50,7 +50,7 @@ def render(voxels: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of the input tensors are not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     voxels = tf.convert_to_tensor(value=voxels)
 
     shape.check_static(
diff --git a/tensorflow_graphics/rendering/volumetric/emission_absorption.py b/tensorflow_graphics/rendering/volumetric/emission_absorption.py
index e646c1c0b..23f4b8c8c 100644
--- a/tensorflow_graphics/rendering/volumetric/emission_absorption.py
+++ b/tensorflow_graphics/rendering/volumetric/emission_absorption.py
@@ -50,7 +50,7 @@ def render(voxels: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of the input tensors are not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     voxels = tf.convert_to_tensor(value=voxels)
 
     shape.check_static(
diff --git a/tensorflow_graphics/rendering/volumetric/ray_radiance.py b/tensorflow_graphics/rendering/volumetric/ray_radiance.py
index cafc0ae8b..b04d9e2dd 100644
--- a/tensorflow_graphics/rendering/volumetric/ray_radiance.py
+++ b/tensorflow_graphics/rendering/volumetric/ray_radiance.py
@@ -47,7 +47,7 @@ def compute_radiance(
     and a tensor of shape `[A1, ..., An, N]` for the sample weights.
   """
 
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     rgba_values = tf.convert_to_tensor(value=rgba_values)
     distances = tf.convert_to_tensor(value=distances)
     distances = tf.expand_dims(distances, -1)
diff --git a/tensorflow_graphics/rendering/volumetric/visual_hull.py b/tensorflow_graphics/rendering/volumetric/visual_hull.py
index ed870621f..ec0328232 100644
--- a/tensorflow_graphics/rendering/volumetric/visual_hull.py
+++ b/tensorflow_graphics/rendering/volumetric/visual_hull.py
@@ -46,7 +46,7 @@ def render(voxels: type_alias.TensorLike,
   Raises:
     ValueError: If the shape of the input tensors are not supported.
   """
-  with tf.name_scope(name):  # pyrefly: ignore[bad-instantiation]
+  with tf.name_scope(name):
     voxels = tf.convert_to_tensor(value=voxels)
 
     shape.check_static(