def coalesce(edge_index, edge_attr=None, num_nodes=None, reduce='add', is_sorted=False, sort_by_row=True):
    'Row-wise sorts :obj:`edge_index` and removes its duplicated entries.\n    Duplicate entries in :obj:`edge_attr` are merged by scattering them\n    together according to the given :obj:`reduce` option.\n\n    Args:\n        edge_index (LongTensor): The edge indices.\n        edge_attr (Tensor or List[Tensor], optional): Edge weights or multi-\n            dimensional edge features.\n            If given as a list, will re-shuffle and remove duplicates for all\n            its entries. (default: :obj:`None`)\n        num_nodes (int, optional): The number of nodes, *i.e.*\n            :obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`)\n        reduce (string, optional): The reduce operation to use for merging edge\n            features (:obj:`"add"`, :obj:`"mean"`, :obj:`"min"`, :obj:`"max"`,\n            :obj:`"mul"`). (default: :obj:`"add"`)\n        is_sorted (bool, optional): If set to :obj:`True`, will expect\n            :obj:`edge_index` to be already sorted row-wise.\n        sort_by_row (bool, optional): If set to :obj:`False`, will sort\n            :obj:`edge_index` column-wise.\n\n    :rtype: :class:`LongTensor` if :attr:`edge_attr` is :obj:`None`, else\n        (:class:`LongTensor`, :obj:`Tensor` or :obj:`List[Tensor]]`)\n    '
    if tlx.is_tensor(edge_index):
        edge_index = tlx.convert_to_numpy(edge_index)
    nnz = edge_index.shape[1]
    num_nodes = maybe_num_nodes(edge_index, num_nodes)
    idx = np.zeros((nnz + 1))
    idx[0] = (- 1)
    idx[1:] = edge_index[(1 - int(sort_by_row))]
    idx[1:] = np.add(np.multiply(idx[1:], num_nodes), edge_index[int(sort_by_row)])
    if (not is_sorted):
        perm = np.argsort(idx[1:])
        idx[1:] = np.sort(idx[1:])
        edge_index = edge_index[:, perm]
        if ((edge_attr is not None) and tlx.ops.is_tensor(edge_attr)):
            edge_attr = tlx.gather(edge_attr, tlx.convert_to_tensor(perm), axis=0)
        elif ((edge_attr is not None) and check_is_numpy(edge_attr)):
            edge_attr = edge_attr[perm]
        elif (edge_attr is not None):
            edge_attr = [tlx.gather(e, perm, axis=0) for e in edge_attr]
    mask = (idx[1:] > idx[:(- 1)])
    if mask.all():
        edge_index = tlx.convert_to_tensor(edge_index, dtype=tlx.int64)
        return (edge_index if (edge_attr is None) else (edge_index, edge_attr))
    edge_index = edge_index[:, mask]
    edge_index = tlx.convert_to_tensor(edge_index, dtype=tlx.int64)
    if (edge_attr is None):
        return edge_index
    idx = np.arange(0, nnz)
    idx = tlx.convert_to_tensor((idx - (1 - mask).cumsum(axis=0)))
    if tlx.ops.is_tensor(edge_attr):
        edge_attr = mpops.segment_sum(edge_attr, idx)
    return (edge_index, edge_attr)
Row-wise sorts :obj:`edge_index` and removes its duplicated entries.
Duplicate entries in :obj:`edge_attr` are merged by scattering them
together according to the given :obj:`reduce` option.

Args:
    edge_index (LongTensor): The edge indices.
    edge_attr (Tensor or List[Tensor], optional): Edge weights or multi-
        dimensional edge features.
        If given as a list, will re-shuffle and remove duplicates for all
        its entries. (default: :obj:`None`)
    num_nodes (int, optional): The number of nodes, *i.e.*
        :obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`)
    reduce (string, optional): The reduce operation to use for merging edge
        features (:obj:`"add"`, :obj:`"mean"`, :obj:`"min"`, :obj:`"max"`,
        :obj:`"mul"`). (default: :obj:`"add"`)
    is_sorted (bool, optional): If set to :obj:`True`, will expect
        :obj:`edge_index` to be already sorted row-wise.
    sort_by_row (bool, optional): If set to :obj:`False`, will sort
        :obj:`edge_index` column-wise.

:rtype: :class:`LongTensor` if :attr:`edge_attr` is :obj:`None`, else
    (:class:`LongTensor`, :obj:`Tensor` or :obj:`List[Tensor]]`)
def coalesce(edge_index, edge_attr=None, num_nodes=None, reduce='add', is_sorted=False, sort_by_row=True):
    'Row-wise sorts :obj:`edge_index` and removes its duplicated entries.\n    Duplicate entries in :obj:`edge_attr` are merged by scattering them\n    together according to the given :obj:`reduce` option.\n\n    Args:\n        edge_index (LongTensor): The edge indices.\n        edge_attr (Tensor or List[Tensor], optional): Edge weights or multi-\n            dimensional edge features.\n            If given as a list, will re-shuffle and remove duplicates for all\n            its entries. (default: :obj:`None`)\n        num_nodes (int, optional): The number of nodes, *i.e.*\n            :obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`)\n        reduce (string, optional): The reduce operation to use for merging edge\n            features (:obj:`"add"`, :obj:`"mean"`, :obj:`"min"`, :obj:`"max"`,\n            :obj:`"mul"`). (default: :obj:`"add"`)\n        is_sorted (bool, optional): If set to :obj:`True`, will expect\n            :obj:`edge_index` to be already sorted row-wise.\n        sort_by_row (bool, optional): If set to :obj:`False`, will sort\n            :obj:`edge_index` column-wise.\n\n    :rtype: :class:`LongTensor` if :attr:`edge_attr` is :obj:`None`, else\n        (:class:`LongTensor`, :obj:`Tensor` or :obj:`List[Tensor]]`)\n    '
    if tlx.is_tensor(edge_index):
        edge_index = tlx.convert_to_numpy(edge_index)
    nnz = edge_index.shape[1]
    num_nodes = maybe_num_nodes(edge_index, num_nodes)
    idx = np.zeros((nnz + 1))
    idx[0] = (- 1)
    idx[1:] = edge_index[(1 - int(sort_by_row))]
    idx[1:] = np.add(np.multiply(idx[1:], num_nodes), edge_index[int(sort_by_row)])
    if (not is_sorted):
        perm = np.argsort(idx[1:])
        idx[1:] = np.sort(idx[1:])
        edge_index = edge_index[:, perm]
        if ((edge_attr is not None) and tlx.ops.is_tensor(edge_attr)):
            edge_attr = tlx.gather(edge_attr, tlx.convert_to_tensor(perm), axis=0)
        elif ((edge_attr is not None) and check_is_numpy(edge_attr)):
            edge_attr = edge_attr[perm]
        elif (edge_attr is not None):
            edge_attr = [tlx.gather(e, perm, axis=0) for e in edge_attr]
    mask = (idx[1:] > idx[:(- 1)])
    if mask.all():
        edge_index = tlx.convert_to_tensor(edge_index, dtype=tlx.int64)
        return (edge_index if (edge_attr is None) else (edge_index, edge_attr))
    edge_index = edge_index[:, mask]
    edge_index = tlx.convert_to_tensor(edge_index, dtype=tlx.int64)
    if (edge_attr is None):
        return edge_index
    idx = np.arange(0, nnz)
    idx = tlx.convert_to_tensor((idx - (1 - mask).cumsum(axis=0)))
    if tlx.ops.is_tensor(edge_attr):
        edge_attr = mpops.segment_sum(edge_attr, idx)
    return (edge_index, edge_attr)