diff --git a/docs/source/conf.py b/docs/source/conf.py
index cd3a28658cb..b06c5d2dd65 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -90,6 +90,7 @@ def __init__(self, src_dir):
         "plot_custom_transforms.py",
         "plot_tv_tensors.py",
         "plot_custom_tv_tensors.py",
+        "plot_transform_tensor_image.py"
     ]
 
     def __call__(self, filename):
diff --git a/gallery/transforms/plot_transform_tensor_image.py b/gallery/transforms/plot_transform_tensor_image.py
new file mode 100644
index 00000000000..1edaae31b7b
--- /dev/null
+++ b/gallery/transforms/plot_transform_tensor_image.py
@@ -0,0 +1,91 @@
+"""
+=======================================
+Converting between tensor and PIL image
+=======================================
+
+.. note::
+    Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_tv_tensors.ipynb>`_
+    or :ref:`go to the end <sphx_glr_download_auto_examples_transforms_plot_custom_tv_tensors.py>` to download the full example code.
+
+In thie example, we explain the basic usgae of :func:`~torchvision.transforms.functional.to_tensor`, :func:`~torchvision.transforms.functional.pil_to_tensor` and :func:`~torchvision.transforms.functional.to_pil_image`.
+And the difference between :func:`~torchvision.transforms.functional.to_tensor` and :func:`~torchvision.transforms.functional.pil_to_tensor`.
+"""
+
+# %%
+# torchvision.transforms.functional.to_tensor
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# In this cell, we illustrate the different representation of a PIL image and tensor
+import PIL.Image as Image
+from torchvision.transforms.functional import to_tensor
+from helpers import plot  # use your favorite visualization library
+
+img_pil = Image.open("../assets/person1.jpg")
+width, height = img_pil.size
+# There is no straight forward way to get channel information
+# Please read https://pillow.readthedocs.io/en/stable/handbook/concepts.html for more detail
+num_channels = 3  # hardcoded since it's a color image.
+print("PIL image: width x height x num_channels:", width, height, num_channels)
+
+img_tensor = to_tensor(img_pil)
+num_channels, height, width = img_tensor.shape
+print("Tensor image: num_channels x height x width:", num_channels, height, width)
+plot([img_tensor])
+
+# %%
+# torchvision.transforms.functional.pil_to_tensor
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# In this cell, we explain the difference between :func:`~torchvision.transforms.functional.pil_to_tensor` vs. :func:`~torchvision.transforms.functional.to_tensor`
+from torchvision.transforms.functional import pil_to_tensor
+
+img_pil = Image.open("../assets/person1.jpg")
+img_to_tensor = to_tensor(img_pil)
+num_channels, height, width = img_to_tensor.shape
+print("Tensor image(to_tensor): num_channels x height x width:", num_channels, height, width)
+print("Tensor image(to_tensor) datatype:", img_to_tensor.dtype)
+img_pil_to_tensor = pil_to_tensor(img_pil)
+num_channels, height, width = img_pil_to_tensor.shape
+print("Tensor image(pil_to_tensor): num_channels x height x width:", num_channels, height, width)
+print("Tensor image(pil_to_tensor) datatype:", img_pil_to_tensor.dtype)
+plot([img_to_tensor, img_pil_to_tensor])
+
+# %%
+# The shape is the same but **data type** is different! The **tensor value** is also different!
+
+# %%
+print(img_to_tensor)  # tensor that is returned by calling to_tensor()
+print(img_pil_to_tensor)  # tensor that is returned by calling pil_to_tensor()
+
+# %%
+# Notice :func:`~torchvision.transforms.functional.to_tensor` automatically scale the image, but :func:`~torchvision.transforms.functional.pil_to_tensor` does not. To rescale the image back,
+
+import torch
+
+img_pil_to_tensor_2 = (img_to_tensor * 255).to(torch.uint8)
+print((img_pil_to_tensor_2 == img_pil_to_tensor).all().item())  # check if two tensors are same
+
+# %%
+# **TLDR** it's recommended to use :func:`~torchvision.transforms.functional.pil_to_tensor` for visualization tasks since most visualization library
+# expects input image to be ``torch.uint8``. On the other hand, :func:`~torchvision.transforms.functional.to_tensor` is better for computation tasks since models, optimizers and loss functions expect
+# input image to be ``torch.float32``.
+
+
+# %%
+# torchvision.transforms.functional.to_pil_image
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# In this cell, we explain an example usage of :func:`~torchvision.transforms.functional.to_pil_image`
+from torchvision.transforms.functional import to_pil_image
+
+img_pil = Image.open("../assets/person1.jpg")
+
+# convert to tensor using to_tensor() and pil_to_tensor()
+img_to_tensor = to_tensor(img_pil)
+img_pil_to_tensor = pil_to_tensor(img_pil)
+
+# convert back to PIL image from tensor
+pil_img_to_tensor = to_pil_image(img_to_tensor)
+pil_img_pil_to_tensor = to_pil_image(img_pil_to_tensor)
+print(pil_img_to_tensor)
+print(pil_img_pil_to_tensor)
+
+# %%
+# Both tensor can be converted back to PIL image.