Convert computational graph diagrams to Mermaid (#3873)

Fixes #3580

## Description
- corrected requires_grad value for z in computational graph
- recreated both computational graph diagrams using Mermaid

## Checklist
<!--- Make sure to add `x` to all items in the following checklist: -->
- [ ] The issue that is being fixed is referred in the description (see
above "Fixes #ISSUE_NUMBER")
- [ ] Only one issue is addressed in this pull request
- [ ] Labels from the issue that this PR is fixing are added to this
pull request
- [ ] No unnecessary issues are included into this pull request.

---------

Co-authored-by: sekyondaMeta <127536312+sekyondaMeta@users.noreply.github.com>

Author: saurabhkthakur

beginner_source/understanding_leaf_vs_nonleaf_tutorial.py

@@ -87,11 +87,26 @@
 #    \cdots \cdot
 #    \frac{\partial \mathbf{f}_1}{\partial \mathbf{x}}
 #
-# .. figure:: /_static/img/understanding_leaf_vs_nonleaf/comp-graph-1.png
-#    :alt: Computational graph after forward pass
-#
-#    Computational graph after forward pass
-#
+# .. mermaid::
+#
+#    graph TD
+#
+#        x["x<br/>is_leaf=True<br/>requires_grad=False<br/>retains_grad=False<br/>grad=None"]
+#        W["W<br/>is_leaf=True<br/>requires_grad=True<br/>retains_grad=False<br/>grad=None"]
+#        b["b<br/>is_leaf=True<br/>requires_grad=True<br/>retains_grad=False<br/>grad=None"]
+#        matmul["x @ W"]
+#        z["z = x @ W + b<br/>is_leaf=False<br/>requires_grad=True<br/>retains_grad=False<br/>grad=None"]
+#        relu["y_pred = relu(z)<br/>is_leaf=False<br/>requires_grad=True<br/>retains_grad=False<br/>grad=None"]
+#        y["y<br/>is_leaf=True<br/>requires_grad=False<br/>retains_grad=False<br/>grad=None"]
+#        loss["loss = mse(y_pred, y)<br/>is_leaf=False<br/>requires_grad=True<br/>retains_grad=False<br/>grad=None"]
+#
+#        x --> matmul
+#        W --> matmul
+#        matmul --> z
+#        b --> z
+#        z --> relu
+#        relu --> loss
+#        y --> loss
 # PyTorch considers a node to be a *leaf* if it is not the result of a
 # tensor operation with at least one input having ``requires_grad=True``
 # (e.g. ``x``, ``W``, ``b``, and ``y``), and everything else to be
@@ -260,11 +275,26 @@
 # convention, this attribute will print ``False`` for any leaf node, even
 # if it requires its gradient.
 #
-# .. figure:: /_static/img/understanding_leaf_vs_nonleaf/comp-graph-2.png
-#    :alt: Computational graph after backward pass
-#
-#    Computational graph after backward pass
-#
+# .. mermaid::
+#
+#    graph TD
+#
+#         x["x<br/>is_leaf=True<br/>requires_grad=False<br/>retains_grad=False<br/>grad=None"]
+#         W["W<br/>is_leaf=True<br/>requires_grad=True<br/>retains_grad=False<br/>grad=torch.Tensor"]
+#         b["b<br/>is_leaf=True<br/>requires_grad=True<br/>retains_grad=False<br/>grad=torch.Tensor"]
+#         matmul["x @ W"]
+#         z["z = x @ W + b<br/>is_leaf=False<br/>requires_grad=True<br/>retains_grad=True<br/>grad=torch.Tensor"]
+#         relu["y_pred = relu(z)<br/>is_leaf=False<br/>requires_grad=True<br/>retains_grad=True<br/>grad=torch.Tensor"]
+#         y["y<br/>is_leaf=True<br/>requires_grad=True<br/>retains_grad=False<br/>grad=None"]
+#         loss["loss = mse(y_pred, y)<br/>is_leaf=False<br/>requires_grad=True<br/>retains_grad=True<br/>grad=torch.Tensor"]
+#
+#         x --> matmul
+#         W --> matmul
+#         matmul --> z
+#         b --> z
+#         z --> relu
+#         relu --> loss
+#         y --> loss
 # If you call ``retain_grad()`` on a leaf tensor, it results in a no-op
 # since leaf tensors already retain their gradients by default (when
 # ``requires_grad=True``).