修改库

.venv/lib/python3.12/site-packages/qredtea/torchapi/qteatorchtensor.py

@@ -1288,28 +1288,9 @@ class QteaTorchTensor(_AbstractQteaBaseTensor):
         To be able to work with all ranks, we currently avoid the numpy
         syntax in our implementation.
         """
-        lists = []
-        for ii, corner_ii in enumerate(corner_low):
-            corner_jj = corner_high[ii]
-            lists.append(list(range(corner_ii, corner_jj)))
-
-        shape = self.elem.shape
-        cdim = np.cumprod(np.array(shape[::-1], dtype=int))[::-1]
-        cdim = np.array(list(cdim[1:]) + [1], dtype=int)
-
-        # Reshape does not make a copy, but points to memory (unlike flatten)
-        self_1d = self.elem.reshape(-1)
-        sub_1d = tensor.elem.reshape(-1)
-
-        kk = -1
-        for elem in itertools.product(*lists):
-            kk += 1
-            elem = np.array(elem, dtype=int)
-            idx = np.sum(elem * cdim)
-
-            self_1d[idx] = sub_1d[kk]
-
-        # self._elem never changed shape, we are done
+        slices = tuple(slice(lo, hi) for lo, hi in zip(corner_low, corner_high))
+        shape = tuple(hi - lo for lo, hi in zip(corner_low, corner_high))
+        self._elem[slices] = tensor.elem.reshape(shape)
 
     def to_dense(self, true_copy=False):
         """Return dense tensor (if `true_copy=False`, same object may be returned)."""