Some more changes; still not working.

src/nested_ragged_tensors/ragged_numpy.py

@@ -589,12 +589,9 @@ def __getitem__(self, idx: int | slice | np.ndarray):
             >>> as_dense['val']
             array([[3.3, 2. , 0. ]])
             >>> as_dense = J[1:2, 1:].to_dense()
-            >>> as_dense['T']
-            array([[5]])
-            >>> as_dense['id']
-            array([[[3, 2, 2]]])
-            >>> as_dense['val']
-            array([[[3.3, 2. , 0. ]]])
+            Traceback (most recent call last):
+                ...
+            ValueError: Multi-level non-int slicing is not supported.
         """
         return self._slice(self._get_slice_indices(idx))
 
@@ -1267,10 +1264,17 @@ def _get_slice_indices(
             case tuple() as T:
                 squeeze_dims = []
                 out_indices = {}
+                seen_non_int = False
                 for dim, idx in enumerate(T):
+                    if seen_non_int:
+                        raise ValueError("Multi-level non-int slicing is not supported.")
+
                     if isinstance(idx, int):
                         idx = slice(idx, idx + 1)
                         squeeze_dims.append(dim)
+                    else:
+                        seen_non_int = True
+
                     if not isinstance(idx, slice):
                         raise TypeError(
                             f"{type(idx)} at index {dim} not supported for "
@@ -1300,16 +1304,17 @@ def _get_slice_indices_internal(
             for key in self.keys_at_dim(0):
                 out[f"dim0/{key}"] = slice(st_i, end_i)
 
-        if f"dim{starting_dim}/bounds" in out:
-            st_i += out[f"dim{starting_dim}/bounds"].start
+        if f"dim{starting_dim+1}/bounds" in out:
+            out_B_slice = out[f"dim{starting_dim+1}/bounds"]
+            st_i += out_B_slice.start
             if end_i is None:
-                end_i = None
+                end_i = out_B_slice.stop
             else:
-                end_i = end_i + out[f"dim{starting_dim}/bounds"].start
-                if out[f"dim{starting_dim}/bounds"].stop is not None:
-                    end_i = min(end_i, out[f"dim{starting_dim}/bounds"].stop)
+                end_i += out_B_slice.start
+                if out_B_slice.stop is not None:
+                    end_i = min(end_i, out_B_slice.stop)
 
-        for dim in range(max(starting_dim, 1), self.max_n_dims):
+        for dim in range(max(starting_dim + 1, 1), self.max_n_dims):
             out[f"dim{dim}/bounds"] = slice(st_i, end_i)
 
             with self._tensor_at_key(f"dim{dim}/bounds") as bounds: