aws_lambda/pytorch/source/torch/functional.py
def chain_matmul(*matrices):
    r"""Returns the matrix product of the :math:`N` 2-D tensors. This product is efficiently computed
    using the matrix chain order algorithm which selects the order in which incurs the lowest cost in terms
    of arithmetic operations (`[CLRS]`_). Note that since this is a function to compute the product, :math:`N`
    needs to be greater than or equal to 2; if equal to 2 then a trivial matrix-matrix product is returned.
    If :math:`N` is 1, then this is a no-op - the original matrix is returned as is.


    Args:
        matrices (Tensors...): a sequence of 2 or more 2-D tensors whose product is to be determined.


    Returns:
        Tensor: if the :math:`i^{th}` tensor was of dimensions :math:`p_{i} \times p_{i + 1}`, then the product
        would be of dimensions :math:`p_{1} \times p_{N + 1}`.

    Example::

        >>> a = torch.randn(3, 4)
        >>> b = torch.randn(4, 5)
        >>> c = torch.randn(5, 6)
        >>> d = torch.randn(6, 7)
        >>> torch.chain_matmul(a, b, c, d)
        tensor([[ -2.3375,  -3.9790,  -4.1119,  -6.6577,   9.5609, -11.5095,  -3.2614],
                [ 21.4038,   3.3378,  -8.4982,  -5.2457, -10.2561,  -2.4684,   2.7163],
                [ -0.9647,  -5.8917,  -2.3213,  -5.2284,  12.8615, -12.2816,  -2.5095]])

    .. _`[CLRS]`: https://mitpress.mit.edu/books/introduction-algorithms-third-edition
    """
    return torch._C._VariableFunctions.chain_matmul(matrices)
r"""Returns the matrix product of the :math:`N` 2-D tensors. This product is efficiently computed
    using the matrix chain order algorithm which selects the order in which incurs the lowest cost in terms
    of arithmetic operations (`[CLRS]`_). Note that since this is a function to compute the product, :math:`N`
    needs to be greater than or equal to 2; if equal to 2 then a trivial matrix-matrix product is returned.
    If :math:`N` is 1, then this is a no-op - the original matrix is returned as is.


    Args:
        matrices (Tensors...): a sequence of 2 or more 2-D tensors whose product is to be determined.


    Returns:
        Tensor: if the :math:`i^{th}` tensor was of dimensions :math:`p_{i} \times p_{i + 1}`, then the product
        would be of dimensions :math:`p_{1} \times p_{N + 1}`.

    Example::

        >>> a = torch.randn(3, 4)
        >>> b = torch.randn(4, 5)
        >>> c = torch.randn(5, 6)
        >>> d = torch.randn(6, 7)
        >>> torch.chain_matmul(a, b, c, d)
        tensor([[ -2.3375,  -3.9790,  -4.1119,  -6.6577,   9.5609, -11.5095,  -3.2614],
                [ 21.4038,   3.3378,  -8.4982,  -5.2457, -10.2561,  -2.4684,   2.7163],
                [ -0.9647,  -5.8917,  -2.3213,  -5.2284,  12.8615, -12.2816,  -2.5095]])

    .. _`[CLRS]`: https://mitpress.mit.edu/books/introduction-algorithms-third-edition
    
r"""Returns the matrix product of the :math:`N` 2-D tensors. This product is efficiently computed
using the matrix chain order algorithm which selects the order in which incurs the lowest cost in terms
of arithmetic operations (`[CLRS]`_). Note that since this is a function to compute the product, :math:`N`
needs to be greater than or equal to 2; if equal to 2 then a trivial matrix-matrix product is returned.
If :math:`N` is 1, then this is a no-op - the original matrix is returned as is.
r " " " Returns the matrix product of the : math : ` N ` 2 - D tensors . This product is efficiently computed using the matrix chain order algorithm which selects the order in which incurs the lowest cost in terms of arithmetic operations ( ` [ CLRS ] ` _ ) . Note that since this is a function to compute the product : math : ` N ` needs to be greater than or equal to 2 ; if equal to 2 then a trivial matrix - matrix product is returned . If : math : ` N ` is 1 then this is a no - op - the original matrix is returned as is .
def chain_matmul(*matrices):
    return torch._C._VariableFunctions.chain_matmul(matrices)
def chain_matmul ( * matrices ) : return torch . _C . _VariableFunctions . chain_matmul ( matrices )
r"""Returns the matrix product of the :math:`N` 2-D tensors.
r " " " Returns the matrix product of the : math : ` N ` 2 - D tensors .
r"""Returns the matrix product of the :math:`N` 2-D tensors. This product is efficiently computed
    using the matrix chain order algorithm which selects the order in which incurs the lowest cost in terms
    of arithmetic operations (`[CLRS]`_). Note that since this is a function to compute the product, :math:`N`
    needs to be greater than or equal to 2; if equal to 2 then a trivial matrix-matrix product is returned.
    If :math:`N` is 1, then this is a no-op - the original matrix is returned as is.


    Args:
        matrices (Tensors...): a sequence of 2 or more 2-D tensors whose product is to be determined.


    Returns:
        Tensor: if the :math:`i^{th}` tensor was of dimensions :math:`p_{i} \times p_{i + 1}`, then the product
        would be of dimensions :math:`p_{1} \times p_{N + 1}`.

    Example::

        >>> a = torch.randn(3, 4)
        >>> b = torch.randn(4, 5)
        >>> c = torch.randn(5, 6)
        >>> d = torch.randn(6, 7)
        >>> torch.chain_matmul(a, b, c, d)
        tensor([[ -2.3375,  -3.9790,  -4.1119,  -6.6577,   9.5609, -11.5095,  -3.2614],
                [ 21.4038,   3.3378,  -8.4982,  -5.2457, -10.2561,  -2.4684,   2.7163],
                [ -0.9647,  -5.8917,  -2.3213,  -5.2284,  12.8615, -12.2816,  -2.5095]])

    .. _`[CLRS]`: https://mitpress.mit.edu/books/introduction-algorithms-third-edition
    """
if the :math:`i^{th}` tensor was of dimensions :math:`p_{i} \times p_{i + 1}`, then the product
would be of dimensions :math:`p_{1} \times p_{N + 1}`.
if the : math : ` i^ { th } ` tensor was of dimensions : math : ` p_ { i } \ times p_ { i + 1 } ` then the product would be of dimensions : math : ` p_ { 1 } \ times p_ { N + 1 } ` .
Tensor
matrices
a sequence of 2 or more 2-D tensors whose product is to be determined.
a sequence of 2 or more 2 - D tensors whose product is to be determined .