[REF] Avoid cyclic import due to type annotations

curvlinops/utils.py

@@ -5,8 +5,6 @@
 from numpy import cumsum
 from torch import Tensor
 
-from curvlinops._torch_base import PyTorchLinearOperator
-
 
 def split_list(x: Union[List, Tuple], sizes: List[int]) -> List[List]:
     """Split a list into multiple lists of specified size.
@@ -70,11 +68,11 @@ def allclose_report(
     return close
 
 
-def assert_is_square(A: Union[Tensor, PyTorchLinearOperator]) -> int:
+def assert_is_square(A) -> int:
     """Assert that a matrix or linear operator is square.
 
     Args:
-        A: Matrix or linear operator to be checked.
+        A: Matrix or linear operator to be checked. Must have a ``.shape`` attribute.
 
     Returns:
         The dimension of the square matrix.
@@ -88,13 +86,11 @@ def assert_is_square(A: Union[Tensor, PyTorchLinearOperator]) -> int:
     return dim
 
 
-def assert_matvecs_subseed_dim(
-    A: Union[Tensor, PyTorchLinearOperator], num_matvecs: int
-):
+def assert_matvecs_subseed_dim(A, num_matvecs: int):
     """Assert that the number of matrix-vector products is smaller than the dimension.
 
     Args:
-        A: Matrix or linear operator to be checked.
+        A: Matrix or linear operator to be checked. Must have a ``.shape`` attribute.
         num_matvecs: Number of matrix-vector products.
 
     Raises: