diff --git a/monai/transforms/io/array.py b/monai/transforms/io/array.py
index 87f9c6676d..2678520ce1 100644
--- a/monai/transforms/io/array.py
+++ b/monai/transforms/io/array.py
@@ -20,7 +20,7 @@
 import warnings
 from pathlib import Path
 from pydoc import locate
-from typing import Dict, List, Optional, Sequence, Union
+from typing import Dict, List, Optional, Sequence, Type, Union
 
 import numpy as np
 import torch
@@ -330,7 +330,7 @@ def __init__(
         separate_folder: bool = True,
         print_log: bool = True,
         output_format: str = "",
-        writer: Union[image_writer.ImageWriter, str, None] = None,
+        writer: Union[Type[image_writer.ImageWriter], str, None] = None,
         channel_dim: Optional[int] = 0,
     ) -> None:
         self.folder_layout = FolderLayout(
diff --git a/monai/transforms/io/dictionary.py b/monai/transforms/io/dictionary.py
index 46dcda469e..d0e2726df0 100644
--- a/monai/transforms/io/dictionary.py
+++ b/monai/transforms/io/dictionary.py
@@ -16,7 +16,7 @@
 """
 
 from pathlib import Path
-from typing import Optional, Union
+from typing import Optional, Type, Union
 
 import numpy as np
 
@@ -237,7 +237,7 @@ def __init__(
         separate_folder: bool = True,
         print_log: bool = True,
         output_format: str = "",
-        writer: Union[image_writer.ImageWriter, str, None] = None,
+        writer: Union[Type[image_writer.ImageWriter], str, None] = None,
     ) -> None:
         super().__init__(keys, allow_missing_keys)
         self.meta_keys = ensure_tuple_rep(meta_keys, len(self.keys))
diff --git a/monai/transforms/utils_pytorch_numpy_unification.py b/monai/transforms/utils_pytorch_numpy_unification.py
index 2718018a10..2dd224b023 100644
--- a/monai/transforms/utils_pytorch_numpy_unification.py
+++ b/monai/transforms/utils_pytorch_numpy_unification.py
@@ -276,7 +276,7 @@ def cumsum(a: NdarrayOrTensor, axis=None, **kwargs) -> NdarrayOrTensor:
     """
 
     if isinstance(a, np.ndarray):
-        return np.cumsum(a, axis)
+        return np.cumsum(a, axis)  # type: ignore
     if axis is None:
         return torch.cumsum(a[:], 0, **kwargs)
     return torch.cumsum(a, dim=axis, **kwargs)