diff --git a/monai/transforms/utility/array.py b/monai/transforms/utility/array.py
index 861907d367..43bf0f336e 100644
--- a/monai/transforms/utility/array.py
+++ b/monai/transforms/utility/array.py
@@ -200,7 +200,7 @@ def __init__(
         self.intensity_range = intensity_range
         self.data_value = data_value
         if additional_info is not None:
-            assert isinstance(additional_info, Callable), "additional_info must be a Callable function."
+            assert callable(additional_info), "additional_info must be a Callable function."
         self.additional_info = additional_info
         self.output = None
         logging.basicConfig(level=logging.NOTSET)
diff --git a/monai/visualize/img2tensorboard.py b/monai/visualize/img2tensorboard.py
index 24b932cdcc..b904d77f50 100644
--- a/monai/visualize/img2tensorboard.py
+++ b/monai/visualize/img2tensorboard.py
@@ -52,7 +52,7 @@ def make_animated_gif_summary(
     max_out: int = 3,
     animation_axes: Sequence[int] = (3,),
     image_axes: Sequence[int] = (1, 2),
-    other_indices: any = None,
+    other_indices: dict = None,
     scale_factor: float = 1,
 ):
     """Creates an animated gif out of an image tensor in 'CHWD' format and returns Summary.
@@ -63,7 +63,7 @@ def make_animated_gif_summary(
         max_out (int): maximum number of slices to animate through
         animation_axes (Sequence[int]): axis to animate on (not currently used)
         image_axes (Sequence[int]): axes of image (not currently used)
-        other_indices (any): (not currently used)
+        other_indices (dict): (not currently used)
         scale_factor (float): amount to multiply values by.
             if the image data is between 0 and 1, using 255 for this value will scale it to displayable range
     """
@@ -83,7 +83,7 @@ def make_animated_gif_summary(
         else:
             other_ind = other_indices.get(i, 0)
             slicing.append(slice(other_ind, other_ind + 1))
-    image: Union[Tensor, np.array] = image[tuple(slicing)]
+    image = image[tuple(slicing)]
 
     for it_i in range(min(max_out, list(image.shape)[0])):
         one_channel_img: Union[Tensor, np.array] = image[it_i, :, :, :].squeeze(dim=0) if torch.is_tensor(
@@ -129,7 +129,7 @@ def add_animated_gif_no_channels(
     global_step: int = None,
 ):
     """Creates an animated gif out of an image tensor in 'HWD' format that does not have
-    a channel dimension and writes it with SummaryWriter. This is similar to the "add_animated_gif" 
+    a channel dimension and writes it with SummaryWriter. This is similar to the "add_animated_gif"
     after inserting a channel dimension of 1.
 
     Args:
diff --git a/setup.cfg b/setup.cfg
index cc72f2b62f..400b18c0ba 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -48,5 +48,19 @@ tag_prefix =
 parentdir_prefix =
 
 [mypy]
-#mypy does not currently pass for monai, so disable with ignore_errors
-ignore_errors = True
+# do not follow imports (except for ones found in typeshed)
+follow_imports=normal
+
+# suppress errors about unsatisfied imports
+ignore_missing_imports=False
+
+# allow returning Any as a consequence of the options above
+warn_return_any=True
+
+# ensure all execution paths are returning
+warn_no_return=True
+
+# lint-style cleanliness for typing needs to be disabled; returns more errors
+# than the full run.
+warn_redundant_casts=True
+warn_unused_ignores=True