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Computes the reverse mode backpropagated gradient of the Cholesky algorithm.

For an explanation see "Differentiation of the Cholesky algorithm" by Iain Murray http://arxiv.org/abs/1602.07527.

### Constants

 String OP_NAME The name of this op, as known by TensorFlow core engine

### Public Methods

 Output () Returns the symbolic handle of the tensor. static CholeskyGrad ( Scope scope, Operand l, Operand grad) Factory method to create a class wrapping a new CholeskyGrad operation. Output () Symmetrized version of df/dA .

## Constants

#### public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

## Public Methods

#### public Output <T> asOutput ()

Returns the symbolic handle of the tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

#### public static CholeskyGrad <T> create ( Scope scope, Operand <T> l, Operand <T> grad)

Factory method to create a class wrapping a new CholeskyGrad operation.

##### Parameters
 scope current scope Output of batch Cholesky algorithm l = cholesky(A). Shape is `[..., M, M]`. Algorithm depends only on lower triangular part of the innermost matrices of this tensor. df/dl where f is some scalar function. Shape is `[..., M, M]`. Algorithm depends only on lower triangular part of the innermost matrices of this tensor.
##### Returns
• a new instance of CholeskyGrad

#### public Output <T> output ()

Symmetrized version of df/dA . Shape is `[..., M, M]`

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