diff --git a/fastMONAI/vision_core.py b/fastMONAI/vision_core.py
index eb9a65b..99fa1a2 100644
--- a/fastMONAI/vision_core.py
+++ b/fastMONAI/vision_core.py
@@ -76,13 +76,13 @@ def _multi_channel(image_paths: list, reorder: bool, resample: list, dtype, only
     tensor = torch.stack([img.data[0] for _, img, _ in image_data], dim=0)
     
     if only_tensor: 
-        dtype(tensor) 
+       return dtype(tensor) 
 
     input_img.set_data(tensor)
     return org_img, input_img, org_size
 
 
-# %% ../nbs/01_vision_core.ipynb 9
+# %% ../nbs/01_vision_core.ipynb 8
 def med_img_reader(
         file_path: (str, Path),
         dtype=torch.Tensor,
@@ -118,15 +118,15 @@ def med_img_reader(
 
     return org_img, input_img, org_size
 
-# %% ../nbs/01_vision_core.ipynb 12
+# %% ../nbs/01_vision_core.ipynb 10
 class MetaResolver(type(torch.Tensor), metaclass=BypassNewMeta):
     '''A class to bypass metaclass conflict:
     https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/data/batch.html
     '''
     pass
 
-# %% ../nbs/01_vision_core.ipynb 13
-class MedBase(torch.Tensor, metaclass=MetaResolver):
+# %% ../nbs/01_vision_core.ipynb 11
+class MedBase(torch.Tensor, metaclass=MetaResolver): 
     '''A class that represents an image object. Metaclass casts x to this class if it is of type cls._bypass_type.'''
 
     _bypass_type=torch.Tensor
@@ -180,12 +180,12 @@ def show(self, ctx=None, channel=0, indices=None, anatomical_plane=0, **kwargs):
     def __repr__(self):
         return f'{self.__class__.__name__} mode={self.mode} size={"x".join([str(d) for d in self.size])}'
 
-# %% ../nbs/01_vision_core.ipynb 14
+# %% ../nbs/01_vision_core.ipynb 12
 class MedImage(MedBase):
     '''Subclass of MedBase that represents an image object.'''
     pass
 
-# %% ../nbs/01_vision_core.ipynb 15
+# %% ../nbs/01_vision_core.ipynb 13
 class MedMask(MedBase):
     '''Subclass of MedBase that represents an mask object.'''
     _show_args = {'alpha':0.5, 'cmap':'tab20'}
diff --git a/nbs/01_vision_core.ipynb b/nbs/01_vision_core.ipynb
index fd14e75..9b9ff91 100644
--- a/nbs/01_vision_core.ipynb
+++ b/nbs/01_vision_core.ipynb
@@ -134,36 +134,12 @@
     "    tensor = torch.stack([img.data[0] for _, img, _ in image_data], dim=0)\n",
     "    \n",
     "    if only_tensor: \n",
-    "        dtype(tensor) \n",
+    "       return dtype(tensor) \n",
     "\n",
     "    input_img.set_data(tensor)\n",
     "    return org_img, input_img, org_size\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# def med_img_reader(file_path:(str, Path), # Image path\n",
-    "#                    dtype=torch.Tensor, # Datatype (MedImage, MedMask, torch.Tensor)\n",
-    "#                    reorder:bool=False, # Whether to reorder the data to be closest to canonical (RAS+) orientation.\n",
-    "#                    resample:list=None, # Whether to resample image to different voxel sizes and image dimensions.\n",
-    "#                    only_tensor:bool=True # Whether to return only image tensor\n",
-    "#                   ):\n",
-    "#     '''Load and preprocess medical image'''\n",
-    "        \n",
-    "#     if isinstance(file_path, str) and ';' in file_path:\n",
-    "#         return _multi_channel(file_path.split(';'), reorder, resample, dtype, only_tensor)\n",
-    "\n",
-    "#     org_img, input_img, org_size = _load_and_preprocess(file_path, reorder, resample, dtype)\n",
-    "\n",
-    "#     if only_tensor: return dtype(input_img.data.type(torch.float)) \n",
-    "        \n",
-    "#     return org_img, input_img, org_size"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -207,48 +183,6 @@
     "    return org_img, input_img, org_size"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def read_medical_image(\n",
-    "        file_path: (str, Path),\n",
-    "        dtype=torch.Tensor,\n",
-    "        reorder: bool = False,\n",
-    "        resample: list = None,\n",
-    "        only_tensor: bool = True\n",
-    "):\n",
-    "    \"\"\"Loads and preprocesses a medical image.\n",
-    "\n",
-    "    Args:\n",
-    "        file_path: Path to the image. Can be a string or a Path object.\n",
-    "        dtype: Datatype for the return value. Defaults to torch.Tensor.\n",
-    "        reorder: Whether to reorder the data to be closest to canonical \n",
-    "            (RAS+) orientation. Defaults to False.\n",
-    "        resample: Whether to resample image to different voxel sizes and \n",
-    "            image dimensions. Defaults to None.\n",
-    "        only_tensor: Whether to return only image tensor. Defaults to True.\n",
-    "\n",
-    "    Returns:\n",
-    "        The preprocessed image. Returns only the image tensor if \n",
-    "        only_tensor is True, otherwise returns original image, \n",
-    "        preprocessed image, and original size.\n",
-    "    \"\"\"\n",
-    "    if isinstance(file_path, str) and ';' in file_path:\n",
-    "        return _multi_channel(\n",
-    "            file_path.split(';'), reorder, resample, dtype, only_tensor)\n",
-    "\n",
-    "    org_img, input_img, org_size = _load_and_preprocess(\n",
-    "        file_path, reorder, resample, dtype)\n",
-    "\n",
-    "    if only_tensor:\n",
-    "        return dtype(input_img.data.type(torch.float))\n",
-    "\n",
-    "    return org_img, input_img, org_size"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -277,7 +211,7 @@
    "outputs": [],
    "source": [
     "#| export\n",
-    "class MedBase(torch.Tensor, metaclass=MetaResolver):\n",
+    "class MedBase(torch.Tensor, metaclass=MetaResolver): \n",
     "    '''A class that represents an image object. Metaclass casts x to this class if it is of type cls._bypass_type.'''\n",
     "\n",
     "    _bypass_type=torch.Tensor\n",