<commit_before>import chainer
from chainer.backends import cuda


class CooMatrix(object):

    """A sparse matrix in COO format.

    Args:
        data (numpy.ndarray or cupy.ndarray): The entries of the matrix.
            The entries are usually non-zero-elements in the matrix.
        row (numpy.ndarray or cupy.ndarray): The row indices of the matrix
            entries.
        col (numpy.ndarray or cupy.ndarray): The column indices of the matrix
            entries.
        shape (tuple of int): The shape of the matrix in dense format.
        order ('C', 'F', 'auto', None): If ``'C'``, the matrix is assumed to be
            made from row-major format (AKA C-contiguous) array, in other words,
            the row indices are sorted. If ``'F'``, the matrix is assumed to be
            made from column-major format (AKA Fortran-contiguous) array, in
            other words, the column indices are sorted. If ``None``, the matrix
            is assumed as neither 'C' order nor 'F' order (this is the default).
            If ``'auto'``, the matrix is automatically checked if it is 'C'
            order, 'F' order or another. This information will be used by some
            functions like sparse_matmul as a hint to improve performance.
        requires_grad (bool): If ``True``, gradient of this sparse matrix will
            be computed in back-propagation.

    .. seealso::
        See :func:`~chainer.utils.to_coo` for how to construct a COO matrix
        from an array.<commit_msg>flake8<commit_after>import chainer
from chainer.backends import cuda


class CooMatrix(object):

    """A sparse matrix in COO format.

    Args:
        data (numpy.ndarray or cupy.ndarray): The entries of the matrix.
            The entries are usually non-zero-elements in the matrix.
        row (numpy.ndarray or cupy.ndarray): The row indices of the matrix
            entries.
        col (numpy.ndarray or cupy.ndarray): The column indices of the matrix
            entries.
        shape (tuple of int): The shape of the matrix in dense format.
        order ('C', 'F', 'auto', None): If ``'C'``, the matrix is assumed to be
            made from row-major format (AKA C-contiguous) array, in other
            words, the row indices are sorted. If ``'F'``, the matrix is
            assumed to be made from column-major format (AKA
            Fortran-contiguous) array, in other words, the column indices are
            sorted. If ``None``, the matrix is assumed as neither 'C' order nor
            'F' order (this is the default). If ``'auto'``, the matrix is
            automatically checked if it is 'C' order, 'F' order or another.
            This information will be used by some functions like sparse_matmul
            as a hint to improve performance.
        requires_grad (bool): If ``True``, gradient of this sparse matrix will
            be computed in back-propagation.

    .. seealso::
        See :func:`~chainer.utils.to_coo` for how to construct a COO matrix
        from an array.
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{'dir': 'git-commits-cleaned', 'id': '6778731', 'n_tokens_mistral': 820, 'n_tokens_neox': 808, 'n_words': 385}