numpy.cumsum(a, axis=None, dtype=None, out=None) → ndarray
cupy.cumsum(a, axis=None, dtype=None, out=None) → ndarray
dask.array.cumsum(x, axis=None, dtype=None, out=None, method='sequential') → ndarray
jax.numpy.cumsum(a, axis=None, dtype=None, out=None) → ndarray
np.cumsum(a, axis=None, dtype=None, out=None) → ndarray
torch.cumsum(input, dim, *, dtype=None, out=None) → Tensor
dim positional/kwarg argument is required.
tf.math.cumsum(x, axis=0, exclusive=False, reverse=False, name=None) → Tensor
Uses exclusive kwarg to indicate whether to include the starting value and exclude the ending value