diff --git a/beginner_source/basics/data_tutorial.py b/beginner_source/basics/data_tutorial.py
index 0ef1fb6b777..ecaad60ef82 100644
--- a/beginner_source/basics/data_tutorial.py
+++ b/beginner_source/basics/data_tutorial.py
@@ -98,8 +98,8 @@
 plt.show()
 
 #################################################################
-# ..
-#  .. figure:: /_static/img/basics/fashion_mnist.png
+#
+# .. image:: ../../_static/img/basics/fashion_mnist.png
 #    :alt: fashion_mnist
 
 
diff --git a/beginner_source/blitz/autograd_tutorial.py b/beginner_source/blitz/autograd_tutorial.py
index 5d468d387f6..0b232d11a51 100644
--- a/beginner_source/blitz/autograd_tutorial.py
+++ b/beginner_source/blitz/autograd_tutorial.py
@@ -239,7 +239,7 @@
 # the arrows are in the direction of the forward pass. The nodes represent the backward functions
 # of each operation in the forward pass. The leaf nodes in blue represent our leaf tensors ``a`` and ``b``.
 #
-# .. figure:: /_static/img/dag_autograd.png
+# .. image:: ../../_static/img/dag_autograd.png
 #
 # .. note::
 #   **DAGs are dynamic in PyTorch**
diff --git a/beginner_source/blitz/cifar10_tutorial.py b/beginner_source/blitz/cifar10_tutorial.py
index 5a9cde3f105..33cc010be90 100644
--- a/beginner_source/blitz/cifar10_tutorial.py
+++ b/beginner_source/blitz/cifar10_tutorial.py
@@ -32,7 +32,7 @@
 ‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of
 size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size.
 
-.. figure:: /_static/img/cifar10.png
+.. image:: ../../_static/img/cifar10.png
    :alt: cifar10
 
    cifar10
diff --git a/beginner_source/blitz/neural_networks_tutorial.py b/beginner_source/blitz/neural_networks_tutorial.py
index 3b3c95fd229..d4407f193a3 100644
--- a/beginner_source/blitz/neural_networks_tutorial.py
+++ b/beginner_source/blitz/neural_networks_tutorial.py
@@ -12,10 +12,10 @@
 
 For example, look at this network that classifies digit images:
 
-.. figure:: /_static/img/mnist.png
+.. image:: ../../_static/img/mnist.png
    :alt: convnet
 
-   convnet
+*convnet*
 
 It is a simple feed-forward network. It takes the input, feeds it
 through several layers one after the other, and then finally gives the
diff --git a/beginner_source/introyt/introyt1_tutorial.py b/beginner_source/introyt/introyt1_tutorial.py
index f52c3902c03..7db2e45f9ce 100644
--- a/beginner_source/introyt/introyt1_tutorial.py
+++ b/beginner_source/introyt/introyt1_tutorial.py
@@ -145,8 +145,8 @@
 
 
 #########################################################################
-# .. figure:: /_static/img/mnist.png
-#    :alt: le-net-5 diagram
+# .. image:: ../../_static/img/mnist.png
+#    :alt: le-net-5 diagram2
 #
 # *Figure: LeNet-5*
 # 
diff --git a/conf.py b/conf.py
index 11ac4459d38..1ef952733b5 100644
--- a/conf.py
+++ b/conf.py
@@ -90,7 +90,8 @@
     'filename_pattern': '.py',
     'ignore_pattern': re.compile(f"({'|'.join(NOT_RUN)}).py$"),
     'promote_jupyter_magic': True,
-    'backreferences_dir': None
+    'backreferences_dir': None,
+    'notebook_images': 'https://pytorch.org/tutorials/'
 }
 
 if os.getenv('GALLERY_PATTERN'):