def structured_dot(x, y):
    """
    Structured Dot is like dot, except that only the
    gradient wrt non-zero elements of the sparse matrix
    `a` are calculated and propagated.

    The output is presumed to be a dense matrix, and is represented by a
    TensorType instance.

    Parameters
    ----------
    a
        A sparse matrix.
    b
        A sparse or dense matrix.

    Returns
    -------
    A sparse matrix
        The dot product of `a` and `b`.

    Notes
    -----
    The grad implemented is structured.

    """

    # @todo: Maybe the triple-transposition formulation (when x is dense)
    # is slow. See if there is a direct way to do this.
    # (JB 20090528: Transposing tensors and sparse matrices is constant-time,
    # inplace, and fast.)

    if hasattr(x, 'getnnz'):
        x = as_sparse_variable(x)
        assert x.format in ["csr", "csc"]
    if hasattr(y, 'getnnz'):
        y = as_sparse_variable(y)
        assert y.format in ["csr", "csc"]

    x_is_sparse_variable = _is_sparse_variable(x)
    y_is_sparse_variable = _is_sparse_variable(y)
    if not x_is_sparse_variable and not y_is_sparse_variable:
        raise TypeError('structured_dot requires at least one sparse argument')

    if x_is_sparse_variable:
        return _structured_dot(x, y)
    else:
        assert y_is_sparse_variable
        return _structured_dot(y.T, x.T).T
dataset/ETHPy150Open rizar/attention-lvcsr/libs/Theano/theano/sparse/basic.py/structured_dot/original