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#include "tensorflow/core/framework/op.h"
namespace tensorflow {
REGISTER_OP("MatrixDeterminant")
.Input("input: T")
.Output("output: T")
.Attr("T: {float, double}")
.Doc(R"doc(
Calculates the determinant of a square matrix.
input: A tensor of shape `[M, M]`.
output: A scalar, equal to the determinant of the input.
T: The type of values in the input and output.
)doc");
REGISTER_OP("BatchMatrixDeterminant")
.Input("input: T")
.Output("output: T")
.Attr("T: {float, double}")
.Doc(R"doc(
Calculates the determinants for a batch of square matrices.
The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
form square matrices. The output is a 1-D tensor containing the determinants
for all input submatrices `[..., :, :]`.
input: Shape is `[..., M, M]`.
output: Shape is `[...]`.
T: The type of values in the input and output.
)doc");
REGISTER_OP("MatrixInverse")
.Input("input: T")
.Output("output: T")
.Attr("T: {float, double}")
.Doc(R"doc(
Calculates the inverse of a square invertible matrix.
The op uses the Cholesky decomposition if the matrix is symmetric positive
definite and LU decomposition with partial pivoting otherwise.
If the matrix is not invertible there is no guarantee what the op does. It
may detect the condition and raise an exception or it may simply return a
garbage result.
input: Shape is `[M, M]`.
output: Shape is `[M, M]` containing the matrix inverse of the input.
T: The type of values in the input and output.
)doc");
REGISTER_OP("BatchMatrixInverse")
.Input("input: T")
.Output("output: T")
.Attr("T: {float, double}")
.Doc(R"doc(
Calculates the inverse of square invertible matrices.
The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
form square matrices. The output is a tensor of the same shape as the input
containing the inverse for all input submatrices `[..., :, :]`.
The op uses the Cholesky decomposition if the matrices are symmetric positive
definite and LU decomposition with partial pivoting otherwise.
If a matrix is not invertible there is no guarantee what the op does. It
may detect the condition and raise an exception or it may simply return a
garbage result.
input: Shape is `[..., M, M]`.
output: Shape is `[..., M, M]`.
T: The type of values in the input and output.
)doc");
REGISTER_OP("Cholesky")
.Input("input: T")
.Output("output: T")
.Attr("T: {double, float}")
.Doc(R"doc(
Calculates the Cholesky decomposition of a square matrix.
The input has to be symmetric and positive definite. Only the lower-triangular
part of the input will be used for this operation. The upper-triangular part
will not be read.
The result is the lower-triangular matrix of the Cholesky decomposition of the
input.
input: Shape is `[M, M]`.
output: Shape is `[M, M]`.
T: The type of values in the input and output.
)doc");
REGISTER_OP("BatchCholesky")
.Input("input: T")
.Output("output: T")
.Attr("T: {double, float}")
.Doc(R"doc(
Calculates the Cholesky decomposition of a batch of square matrices.
The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
form square matrices, with the same constraints as the single matrix Cholesky
decomposition above. The output is a tensor of the same shape as the input
containing the Cholesky decompositions for all input submatrices `[..., :, :]`.
input: Shape is `[..., M, M]`.
output: Shape is `[..., M, M]`.
T: The type of values in the input and output.
)doc");
} // namespace tensorflow
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