def add(x, y):
    """
    Add two matrices, at least one of which is sparse.

    This method will provide the right op according
    to the inputs.

    Parameters
    ----------
    x
        A matrix variable.
    y
        A matrix variable.

    Returns
    -------
    A sparse matrix
        `x` + `y`

    Notes
    -----
    At least one of `x` and `y` must be a sparse matrix.

    The grad will be structured only when one of the variable will be a dense
    matrix.

    """

    if hasattr(x, 'getnnz'):
        x = as_sparse_variable(x)
    if hasattr(y, 'getnnz'):
        y = as_sparse_variable(y)
    if not isinstance(x, theano.Variable):
        x = theano.tensor.as_tensor_variable(x)
    if not isinstance(y, theano.Variable):
        y = theano.tensor.as_tensor_variable(y)

    x_is_sparse_variable = _is_sparse_variable(x)
    y_is_sparse_variable = _is_sparse_variable(y)

    assert x_is_sparse_variable or y_is_sparse_variable
    if x_is_sparse_variable and y_is_sparse_variable:
        return add_s_s(x, y)
    elif x_is_sparse_variable and not y_is_sparse_variable:
        return add_s_d(x, y)
    elif y_is_sparse_variable and not x_is_sparse_variable:
        return add_s_d(y, x)
    else:
        raise NotImplementedError()
dataset/ETHPy150Open rizar/attention-lvcsr/libs/Theano/theano/sparse/basic.py/add/original