Cannot cast array data from dtype('uint64') to dtype('int64') according to the rule 'safe'

[KevinLikesDringCoffe]

I'm running sparse tucker examples in [07_pydata_sparse_backend]. However when executing tensor.max() , TypeError is reported.

TypeError                                 Traceback (most recent call last)
Input In [9], in <cell line: 2>()
      1 # The most frequently a word has been used in a single paper
----> 2 tensor.max()

File ~/bench/sparse/sparse/_sparse_array.py:444, in SparseArray.max(self, axis, keepdims, out)
    421 def max(self, axis=None, keepdims=False, out=None):
    422     """
    423     Maximize along the given axes. Uses all axes by default.
    424 
   (...)
    442     scipy.sparse.coo_matrix.max : Equivalent Scipy function.
    443     """
--> 444     return np.maximum.reduce(self, out=out, axis=axis, keepdims=keepdims)

File ~/bench/sparse/sparse/_sparse_array.py:307, in SparseArray.__array_ufunc__(self, ufunc, method, *inputs, **kwargs)
    305     result = elemwise(ufunc, *inputs, **kwargs)
    306 elif method == "reduce":
--> 307     result = SparseArray._reduce(ufunc, *inputs, **kwargs)
    308 else:
    309     return NotImplemented

File ~/bench/sparse/sparse/_sparse_array.py:278, in SparseArray._reduce(method, *args, **kwargs)
    275 if isinstance(self, ss.spmatrix):
    276     self = type(self).from_scipy_sparse(self)
--> 278 return self.reduce(method, **kwargs)

File ~/bench/sparse/sparse/_sparse_array.py:360, in SparseArray.reduce(self, method, axis, keepdims, **kwargs)
    358 if not isinstance(axis, tuple):
    359     axis = (axis,)
--> 360 out = self._reduce_calc(method, axis, keepdims, **kwargs)
    361 if len(out) == 1:
    362     return out[0]

File ~/bench/sparse/sparse/_coo/core.py:698, in COO._reduce_calc(self, method, axis, keepdims, **kwargs)
    691 a = self.transpose(neg_axis + axis)
    692 a = a.reshape(
    693     (
    694         np.prod([self.shape[d] for d in neg_axis], dtype=np.intp),
    695         np.prod([self.shape[d] for d in axis], dtype=np.intp),
    696     )
    697 )
--> 698 data, inv_idx, counts = _grouped_reduce(a.data, a.coords[0], method, **kwargs)
    699 n_cols = a.shape[1]
    700 arr_attrs = (a, neg_axis, inv_idx)

File ~/bench/sparse/sparse/_coo/core.py:1574, in _grouped_reduce(x, groups, method, **kwargs)
   1571 # Partial credit to @shoyer
   1572 # Ref: https://gist.github.com/shoyer/f538ac78ae904c936844
   1573 inv_idx, counts = _calc_counts_invidx(groups)
-> 1574 result = method.reduceat(x, inv_idx, **kwargs)
   1575 return result, inv_idx, counts

TypeError: Cannot cast array data from dtype('uint64') to dtype('int64') according to the rule 'safe'

How can I solve this problem?