diff --git a/monai/transforms/transform.py b/monai/transforms/transform.py
index 3ea2fda136..61794308f4 100644
--- a/monai/transforms/transform.py
+++ b/monai/transforms/transform.py
@@ -40,9 +40,9 @@ def _apply_transform(
     Otherwise `parameters` is considered as single argument to `transform`.
 
     Args:
-        transform (Callable[..., ReturnType]): a callable to be used to transform `data`.
-        parameters (Any): parameters for the `transform`.
-        unpack_parameters (bool, optional): whether to unpack parameters for `transform`. Defaults to False.
+        transform: a callable to be used to transform `data`.
+        parameters: parameters for the `transform`.
+        unpack_parameters: whether to unpack parameters for `transform`. Defaults to False.
 
     Returns:
         ReturnType: The return type of `transform`.
@@ -64,11 +64,11 @@ def apply_transform(
     otherwise transform will be applied once with `data` as the argument.
 
     Args:
-        transform (Callable[..., ReturnType]): a callable to be used to transform `data`.
-        data (Any): an object to be transformed.
-        map_items (bool, optional): whether to apply transform to each item in `data`,
+        transform: a callable to be used to transform `data`.
+        data: an object to be transformed.
+        map_items: whether to apply transform to each item in `data`,
             if `data` is a list or tuple. Defaults to True.
-        unpack_items (bool, optional): [description]. Defaults to False.
+        unpack_items: whether to unpack parameters using `*`. Defaults to False.
 
     Raises:
         Exception: When ``transform`` raises an exception.
@@ -216,17 +216,15 @@ def __call__(self, data: Any):
         return an updated version of ``data``.
         To simplify the input validations, most of the transforms assume that
 
-        - ``data`` is a Numpy ndarray, PyTorch Tensor or string
+        - ``data`` is a Numpy ndarray, PyTorch Tensor or string,
         - the data shape can be:
 
-          #. string data without shape, `LoadImage` transform expects file paths
-          #. most of the pre-processing transforms expect: ``(num_channels, spatial_dim_1[, spatial_dim_2, ...])``,
-             except that `AddChannel` expects (spatial_dim_1[, spatial_dim_2, ...]) and
-             `AsChannelFirst` expects (spatial_dim_1[, spatial_dim_2, ...], num_channels)
-          #. most of the post-processing transforms expect
-             ``(batch_size, num_channels, spatial_dim_1[, spatial_dim_2, ...])``
+          #. string data without shape, `LoadImage` transform expects file paths,
+          #. most of the pre-/post-processing transforms expect: ``(num_channels, spatial_dim_1[, spatial_dim_2, ...])``,
+             except for example: `AddChannel` expects (spatial_dim_1[, spatial_dim_2, ...]) and
+             `AsChannelFirst` expects (spatial_dim_1[, spatial_dim_2, ...], num_channels),
 
-        - the channel dimension is not omitted even if number of channels is one
+        - the channel dimension is often not omitted even if number of channels is one.
 
         This method can optionally take additional arguments to help execute transformation operation.
 
@@ -323,18 +321,16 @@ def __call__(self, data):
 
         To simplify the input validations, this method assumes:
 
-        - ``data`` is a Python dictionary
+        - ``data`` is a Python dictionary,
         - ``data[key]`` is a Numpy ndarray, PyTorch Tensor or string, where ``key`` is an element
           of ``self.keys``, the data shape can be:
 
-          #. string data without shape, `LoadImaged` transform expects file paths
-          #. most of the pre-processing transforms expect: ``(num_channels, spatial_dim_1[, spatial_dim_2, ...])``,
-             except that `AddChanneld` expects (spatial_dim_1[, spatial_dim_2, ...]) and
+          #. string data without shape, `LoadImaged` transform expects file paths,
+          #. most of the pre-/post-processing transforms expect: ``(num_channels, spatial_dim_1[, spatial_dim_2, ...])``,
+             except for example: `AddChanneld` expects (spatial_dim_1[, spatial_dim_2, ...]) and
              `AsChannelFirstd` expects (spatial_dim_1[, spatial_dim_2, ...], num_channels)
-          #. most of the post-processing transforms expect
-             ``(batch_size, num_channels, spatial_dim_1[, spatial_dim_2, ...])``
 
-        - the channel dimension is not omitted even if number of channels is one
+        - the channel dimension is often not omitted even if number of channels is one.
 
         Raises:
             NotImplementedError: When the subclass does not override this method.
diff --git a/tests/test_decollate.py b/tests/test_decollate.py
index 9bc3c3b470..f35988e215 100644
--- a/tests/test_decollate.py
+++ b/tests/test_decollate.py
@@ -120,7 +120,7 @@ def check_match(self, in1, in2):
 
     def check_decollate(self, dataset):
         batch_size = 2
-        num_workers = 2
+        num_workers = 2 if sys.platform == "linux" else 0
 
         loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
 
diff --git a/tests/test_inverse_collation.py b/tests/test_inverse_collation.py
index f0a87c2b9d..d04360a95d 100644
--- a/tests/test_inverse_collation.py
+++ b/tests/test_inverse_collation.py
@@ -106,7 +106,7 @@ def test_collation(self, _, transform, collate_fn, ndim):
             modified_transform = Compose([transform, ResizeWithPadOrCropd(KEYS, 100), ToTensord(KEYS)])
 
         # num workers = 0 for mac or gpu transforms
-        num_workers = 0 if sys.platform == "darwin" or torch.cuda.is_available() else 2
+        num_workers = 0 if sys.platform != "linux" or torch.cuda.is_available() else 2
 
         dataset = CacheDataset(data, transform=modified_transform, progress=False)
         loader = DataLoader(dataset, num_workers, batch_size=self.batch_size, collate_fn=collate_fn)