Updated generated linop files

PiperOrigin-RevId: 377130710

Author: sharadmv

tensorflow_probability/python/internal/backend/numpy/gen/linear_operator.py

@@ -265,7 +265,7 @@ def __init__(self,
     self._name = name or type(self).__name__
 
   @contextlib.contextmanager
-  def _name_scope(self, name=None):
+  def _name_scope(self, name=None):  # pylint: disable=method-hidden
     """Helper function to standardize op scope."""
     full_name = self.name
     if name is not None:
@@ -357,7 +357,7 @@ def shape_tensor(self, name="shape_tensor"):
     Returns:
       `int32` `Tensor`
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       # Prefer to use statically defined shape if available.
       if tensor_shape.TensorShape(self.shape).is_fully_defined():
         return linear_operator_util.shape_tensor(tensor_shape.TensorShape(self.shape).as_list())
@@ -392,7 +392,7 @@ def batch_shape_tensor(self, name="batch_shape_tensor"):
       `int32` `Tensor`
     """
     # Derived classes get this "for free" once .shape() is implemented.
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return self._batch_shape_tensor()
 
   def _batch_shape_tensor(self, shape=None):
@@ -419,7 +419,7 @@ def tensor_rank(self, name="tensor_rank"):
       Python integer, or None if the tensor rank is undefined.
     """
     # Derived classes get this "for free" once .shape() is implemented.
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return tensor_shape.TensorShape(self.shape).ndims
 
   def tensor_rank_tensor(self, name="tensor_rank_tensor"):
@@ -435,7 +435,7 @@ def tensor_rank_tensor(self, name="tensor_rank_tensor"):
       `int32` `Tensor`, determined at runtime.
     """
     # Derived classes get this "for free" once .shape() is implemented.
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return self._tensor_rank_tensor()
 
   def _tensor_rank_tensor(self, shape=None):
@@ -478,7 +478,7 @@ def domain_dimension_tensor(self, name="domain_dimension_tensor"):
       `int32` `Tensor`
     """
     # Derived classes get this "for free" once .shape() is implemented.
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return self._domain_dimension_tensor()
 
   def _domain_dimension_tensor(self, shape=None):
@@ -522,7 +522,7 @@ def range_dimension_tensor(self, name="range_dimension_tensor"):
       `int32` `Tensor`
     """
     # Derived classes get this "for free" once .shape() is implemented.
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return self._range_dimension_tensor()
 
   def _range_dimension_tensor(self, shape=None):
@@ -581,7 +581,7 @@ def assert_non_singular(self, name="assert_non_singular"):
       An `Assert` `Op`, that, when run, will raise an `InvalidArgumentError` if
         the operator is singular.
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return self._assert_non_singular()
 
   def _assert_positive_definite(self):
@@ -614,7 +614,7 @@ def assert_positive_definite(self, name="assert_positive_definite"):
       An `Assert` `Op`, that, when run, will raise an `InvalidArgumentError` if
         the operator is not positive definite.
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return self._assert_positive_definite()
 
   def _assert_self_adjoint(self):
@@ -640,7 +640,7 @@ def assert_self_adjoint(self, name="assert_self_adjoint"):
       An `Assert` `Op`, that, when run, will raise an `InvalidArgumentError` if
         the operator is not self-adjoint.
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return self._assert_self_adjoint()
 
   def _check_input_dtype(self, arg):
@@ -694,10 +694,10 @@ def matmul(self, x, adjoint=False, adjoint_arg=False, name="matmul"):
             "Operators are incompatible. Expected `x` to have dimension"
             " {} but got {}.".format(
                 left_operator.domain_dimension, right_operator.range_dimension))
-      with self._name_scope(name):
+      with self._name_scope(name):  # pylint: disable=not-callable
         return linear_operator_algebra.matmul(left_operator, right_operator)
 
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       x = ops.convert_to_tensor(x, name="x")
       # self._check_input_dtype(x)
 
@@ -744,7 +744,7 @@ def matvec(self, x, adjoint=False, name="matvec"):
     Returns:
       A `Tensor` with shape `[..., M]` and same `dtype` as `self`.
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       x = ops.convert_to_tensor(x, name="x")
       # self._check_input_dtype(x)
       self_dim = -2 if adjoint else -1
@@ -776,7 +776,7 @@ def determinant(self, name="det"):
       raise NotImplementedError(
           "Determinant not implemented for an operator that is expected to "
           "not be square.")
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return self._determinant()
 
   def _log_abs_determinant(self):
@@ -805,7 +805,7 @@ def log_abs_determinant(self, name="log_abs_det"):
       raise NotImplementedError(
           "Determinant not implemented for an operator that is expected to "
           "not be square.")
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return self._log_abs_determinant()
 
   def _dense_solve(self, rhs, adjoint=False, adjoint_arg=False):
@@ -887,10 +887,10 @@ def solve(self, rhs, adjoint=False, adjoint_arg=False, name="solve"):
             "Operators are incompatible. Expected `rhs` to have dimension"
             " {} but got {}.".format(
                 left_operator.domain_dimension, right_operator.range_dimension))
-      with self._name_scope(name):
+      with self._name_scope(name):  # pylint: disable=not-callable
         return linear_operator_algebra.solve(left_operator, right_operator)
 
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       rhs = ops.convert_to_tensor(rhs, name="rhs")
       # self._check_input_dtype(rhs)
 
@@ -947,7 +947,7 @@ def solvevec(self, rhs, adjoint=False, name="solve"):
     Raises:
       NotImplementedError:  If `self.is_non_singular` or `is_square` is False.
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       rhs = ops.convert_to_tensor(rhs, name="rhs")
       # self._check_input_dtype(rhs)
       self_dim = -1 if adjoint else -2
@@ -970,7 +970,7 @@ def adjoint(self, name="adjoint"):
     """
     if self.is_self_adjoint is True:  # pylint: disable=g-bool-id-comparison
       return self
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return linear_operator_algebra.adjoint(self)
 
   # self.H is equivalent to self.adjoint().
@@ -998,7 +998,7 @@ def inverse(self, name="inverse"):
       raise ValueError("Cannot take the Inverse: This operator represents "
                        "a singular matrix.")
 
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return linear_operator_algebra.inverse(self)
 
   def cholesky(self, name="cholesky"):
@@ -1023,7 +1023,7 @@ def cholesky(self, name="cholesky"):
     if not self._can_use_cholesky():
       raise ValueError("Cannot take the Cholesky decomposition: "
                        "Not a positive definite self adjoint matrix.")
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return linear_operator_algebra.cholesky(self)
 
   def _to_dense(self):
@@ -1044,7 +1044,7 @@ def _to_dense(self):
 
   def to_dense(self, name="to_dense"):
     """Return a dense (batch) matrix representing this operator."""
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return self._to_dense()
 
   def _diag_part(self):
@@ -1076,7 +1076,7 @@ def diag_part(self, name="diag_part"):
     Returns:
       diag_part:  A `Tensor` of same `dtype` as self.
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return self._diag_part()
 
   def _trace(self):
@@ -1093,7 +1093,7 @@ def trace(self, name="trace"):
     Returns:
       Shape `[B1,...,Bb]` `Tensor` of same `dtype` as `self`.
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return self._trace()
 
   def _add_to_tensor(self, x):
@@ -1110,7 +1110,7 @@ def add_to_tensor(self, x, name="add_to_tensor"):
     Returns:
       A `Tensor` with broadcast shape and same `dtype` as `self`.
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       x = ops.convert_to_tensor(x, name="x")
       # self._check_input_dtype(x)
       return self._add_to_tensor(x)
@@ -1134,7 +1134,7 @@ def eigvals(self, name="eigvals"):
     """
     if not self.is_self_adjoint:
       raise NotImplementedError("Only self-adjoint matrices are supported.")
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return self._eigvals()
 
   def _cond(self):
@@ -1159,7 +1159,7 @@ def cond(self, name="cond"):
     Returns:
       Shape `[B1,...,Bb]` `Tensor` of same `dtype` as `self`.
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       return self._cond()
 
   def _can_use_cholesky(self):

tensorflow_probability/python/internal/backend/numpy/gen/linear_operator_block_diag.py

@@ -366,10 +366,10 @@ class docstring for definition of shape compatibility.
             "Operators are incompatible. Expected `x` to have dimension"
             " {} but got {}.".format(
                 left_operator.domain_dimension, right_operator.range_dimension))
-      with self._name_scope(name):
+      with self._name_scope(name):  # pylint: disable=not-callable
         return linear_operator_algebra.matmul(left_operator, right_operator)
 
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       arg_dim = -1 if adjoint_arg else -2
       block_dimensions = (self._block_range_dimensions() if adjoint
                           else self._block_domain_dimensions())
@@ -446,7 +446,7 @@ def matvec(self, x, adjoint=False, name="matvec"):
     Returns:
       A `Tensor` with shape `[..., M]` and same `dtype` as `self`.
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       block_dimensions = (self._block_range_dimensions() if adjoint
                           else self._block_domain_dimensions())
       if linear_operator_util.arg_is_blockwise(block_dimensions, x, -1):
@@ -547,10 +547,10 @@ def solve(self, rhs, adjoint=False, adjoint_arg=False, name="solve"):
             "Operators are incompatible. Expected `rhs` to have dimension"
             " {} but got {}.".format(
                 left_operator.domain_dimension, right_operator.range_dimension))
-      with self._name_scope(name):
+      with self._name_scope(name):  # pylint: disable=not-callable
         return linear_operator_algebra.solve(left_operator, right_operator)
 
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       block_dimensions = (self._block_domain_dimensions() if adjoint
                           else self._block_range_dimensions())
       arg_dim = -1 if adjoint_arg else -2
@@ -630,7 +630,7 @@ def solvevec(self, rhs, adjoint=False, name="solve"):
     Raises:
       NotImplementedError:  If `self.is_non_singular` or `is_square` is False.
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       block_dimensions = (self._block_domain_dimensions() if adjoint
                           else self._block_range_dimensions())
       if linear_operator_util.arg_is_blockwise(block_dimensions, rhs, -1):

tensorflow_probability/python/internal/backend/numpy/gen/linear_operator_block_lower_triangular.py

@@ -453,10 +453,10 @@ class docstring for definition of shape compatibility.
             "Operators are incompatible. Expected `x` to have dimension"
             " {} but got {}.".format(
                 left_operator.domain_dimension, right_operator.range_dimension))
-      with self._name_scope(name):
+      with self._name_scope(name):  # pylint: disable=not-callable
         return linear_operator_algebra.matmul(left_operator, right_operator)
 
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       arg_dim = -1 if adjoint_arg else -2
       block_dimensions = (self._block_range_dimensions() if adjoint
                           else self._block_domain_dimensions())
@@ -567,7 +567,7 @@ def matvec(self, x, adjoint=False, name="matvec"):
     Returns:
       A `Tensor` with shape `[..., M]` and same `dtype` as `self`.
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       block_dimensions = (self._block_range_dimensions() if adjoint
                           else self._block_domain_dimensions())
       if linear_operator_util.arg_is_blockwise(block_dimensions, x, -1):
@@ -692,10 +692,10 @@ def solve(self, rhs, adjoint=False, adjoint_arg=False, name="solve"):
             "Operators are incompatible. Expected `rhs` to have dimension"
             " {} but got {}.".format(
                 left_operator.domain_dimension, right_operator.range_dimension))
-      with self._name_scope(name):
+      with self._name_scope(name):  # pylint: disable=not-callable
         return linear_operator_algebra.solve(left_operator, right_operator)
 
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       block_dimensions = (self._block_domain_dimensions() if adjoint
                           else self._block_range_dimensions())
       arg_dim = -1 if adjoint_arg else -2
@@ -819,7 +819,7 @@ def solvevec(self, rhs, adjoint=False, name="solve"):
     Raises:
       NotImplementedError:  If `self.is_non_singular` or `is_square` is False.
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       block_dimensions = (self._block_domain_dimensions() if adjoint
                           else self._block_range_dimensions())
       if linear_operator_util.arg_is_blockwise(block_dimensions, rhs, -1):

tensorflow_probability/python/internal/backend/numpy/gen/linear_operator_circulant.py

@@ -327,7 +327,7 @@ def convolution_kernel(self, name="convolution_kernel"):
     Returns:
       `Tensor` with `dtype` `self.dtype`.
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       h = self._ifft(_to_complex(self.spectrum))
       return _ops.cast(h, self.dtype)
 
@@ -373,7 +373,7 @@ def assert_hermitian_spectrum(self, name="assert_hermitian_spectrum"):
       An `Op` that asserts this operator has Hermitian spectrum.
     """
     eps = np.finfo(dtypes.real_dtype(self.dtype)).eps
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       # Assume linear accumulation of error.
       max_err = eps * self.domain_dimension_tensor()
       imag_convolution_kernel = math_ops.imag(self.convolution_kernel())

tensorflow_probability/python/internal/backend/numpy/gen/linear_operator_householder.py

@@ -246,7 +246,7 @@ def _trace(self):
 
   def _determinant(self):
     # For householder transformations, the determinant is -1.
-    return -array_ops.ones(shape=self.batch_shape_tensor(), dtype=self.dtype)
+    return -array_ops.ones(shape=self.batch_shape_tensor(), dtype=self.dtype)  # pylint: disable=invalid-unary-operand-type
 
   def _log_abs_determinant(self):
     # Orthogonal matrix -> log|Q| = 0.
@@ -280,7 +280,7 @@ def _eigvals(self):
     neg_shape = array_ops.concat([result_shape[:-1], [1]], axis=-1)
     eigvals = array_ops.ones(shape=ones_shape, dtype=self.dtype)
     eigvals = array_ops.concat(
-        [-array_ops.ones(shape=neg_shape, dtype=self.dtype), eigvals], axis=-1)
+        [-array_ops.ones(shape=neg_shape, dtype=self.dtype), eigvals], axis=-1)  # pylint: disable=invalid-unary-operand-type
     return eigvals
 
   def _cond(self):

tensorflow_probability/python/internal/backend/numpy/gen/linear_operator_identity.py

@@ -425,7 +425,7 @@ def add_to_tensor(self, mat, name="add_to_tensor"):
     Returns:
       A `Tensor` with broadcast shape and same `dtype` as `self`.
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       mat = ops.convert_to_tensor(mat, name="mat")
       mat_diag = _linalg.diag_part(mat)
       new_diag = 1 + mat_diag
@@ -767,7 +767,7 @@ def add_to_tensor(self, mat, name="add_to_tensor"):
     Returns:
       A `Tensor` with broadcast shape and same `dtype` as `self`.
     """
-    with self._name_scope(name):
+    with self._name_scope(name):  # pylint: disable=not-callable
       # Shape [B1,...,Bb, 1]
       multiplier_vector = array_ops.expand_dims(self.multiplier, -1)