Modify rule S6929: add the PyTorch library (#3984)

docs/header_names/allowed_framework_names.adoc

@@ -123,6 +123,7 @@
 * SignXML
 * SQLAlchemy
 * ssl
+* TensorFlow
 // Docker
 * Wget
 // Cloudformation

rules/S6929/python/metadata.json

@@ -1,5 +1,5 @@
 {
-  "title": "The axis argument should be specified when using TensorFlow's reduction operations",
+  "title": "The reduction axis/dimension should be specified when using reduction operations",
   "type": "CODE_SMELL",
   "status": "ready",
   "remediation": {
@@ -7,13 +7,17 @@
     "constantCost": "5min"
   },
   "tags": [
+    "tensorflow",
+    "pytorch",
+    "machine-learning",
+    "scientific-computing"
   ],
   "defaultSeverity": "Major",
   "ruleSpecification": "RSPEC-6929",
   "sqKey": "S6929",
   "scope": "All",
   "defaultQualityProfiles": ["Sonar way"],
-  "quickfix": "unknown",
+  "quickfix": "targeted",
   "code": {
     "impacts": {
       "MAINTAINABILITY": "MEDIUM",

rules/S6929/python/rule.adoc

@@ -1,8 +1,10 @@
-This rule raises an issue when the axis argument is not provided to TensorFlow's reduction operations.
+This rule raises an issue when the `axis`/`dim`` argument is not provided to reduction operations.
 
 == Why is this an issue?
 
-The result of TensorFlow's reduction operations (i.e. ``tf.math.reduce_sum``, ``tf.math.reduce_std``), 
+=== TensorFlow
+
+The result of reduction operations (i.e. ``tf.math.reduce_sum``, ``tf.math.reduce_std``, ``torch.sum``, ``torch.mean``, etc...),
 highly depends on the shape of the Tensor provided. 
 
 [source,python]
@@ -61,7 +63,11 @@ In the example above, specifying the axis clarifies the intent, as the result no
 reduce across all dimensions the user should provide the list of axis `axis=[0,1]` 
 or clearly state the default behavior should be applied with ``axis=None``.
 
-== How to fix it
+=== The PyTorch equivalent
+
+The same behavior occurs in PyTorch, but the argument is called `dim` instead of `axis`.
+
+== How to fix it in TensorFlow
 
 To fix this issue provide the axis argument when using a TensorFlow reduction operation such as ``tf.math.reduce_sum``, ``tf.math.reduce_prod``, ``tf.math.reduce_mean``, etc...
 
@@ -88,6 +94,32 @@ tf.math.reduce_sum(x, axis=0) # Compliant: the reduction will happen only on the
 ----
 
 
+== How to fix it in PyTorch
+
+To fix this issue provide the dim argument when using a PyTorch reduction operation such as ``torch.sum``, ``torch.prod``, ``torch.mean``, etc...
+
+=== Code examples
+
+==== Noncompliant code example
+
+[source,python,diff-id=2,diff-type=noncompliant]
+----
+import torch
+
+x = torch.tensor([[1, 1, 1], [1, 1, 1]])
+torch.sum(x) # Noncompliant: the dim argument defaults to None
+----
+
+==== Compliant solution
+
+[source,python,diff-id=2,diff-type=compliant]
+----
+import torch
+
+x = torch.tensor([[1, 1, 1], [1, 1, 1]])
+torch.sum(x, dim=None) # Compliant: all dimensions will be reduced
+----
+
 == Resources
 === Documentation
 
@@ -99,6 +131,8 @@ tf.math.reduce_sum(x, axis=0) # Compliant: the reduction will happen only on the
 * TensorFlow Documentation - https://www.tensorflow.org/api_docs/python/tf/math/reduce_sum[tf.math.reduce_sum reference]
 * TensorFlow Documentation - https://www.tensorflow.org/api_docs/python/tf/math/reduce_variance[tf.math.reduce_variance reference]
 
+* PyTorch Documentation - https://pytorch.org/docs/stable/torch.html#reduction-ops[Reduction operations]
+
 === Articles & blog posts
 
 * Vahidk Developers Guide - https://github.com/vahidk/EffectiveTensorflow?tab=readme-ov-file#broadcasting-the-good-and-the-ugly[Broadcasting the good and the ugly]