# sum¶

paddle.sparse. sum ( x, axis=None, dtype=None, keepdim=False, name=None ) [source]

Computes the sum of sparse tensor elements over the given dimension, requiring x to be a SparseCooTensor or SparseCsrTensor.

Parameters
• x (Tensor) – An N-D Tensor, the data type is bool, float16, float32, float64, int32 or int64.

• axis (int|list|tuple, optional) – The dimensions along which the sum is performed. If `None`, sum all elements of `x` and return a Tensor with a single element, otherwise must be in the range \([-rank(x), rank(x))\). If \(axis[i] < 0\), the dimension to reduce is \(rank + axis[i]\).

• dtype (str, optional) – The dtype of output Tensor. The default value is None, the dtype of output is the same as input Tensor x.

• keepdim (bool, optional) – Whether to reserve the reduced dimension in the output Tensor. The result Tensor will have one fewer dimension than the `x` unless `keepdim` is true, default value is False.

• name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

Results of summation operation on the specified axis of input Tensor x. if x.dtype=’bool’ or x.dtype=’int32’, it’s data type is ‘int64’, otherwise it’s data type is the same as x.

Return type

Tensor

Examples

```>>> import paddle

>>> dense_x = paddle.to_tensor([[-2., 0.], [1., 2.]])
>>> sparse_x = dense_x.to_sparse_coo(1)
>>> out1
indices=[0],
values=1.)
>>> out2
indices=[[0]],
values=[[-1.,  2.]])