Create rule S6922

rules/S6922/metadata.json

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+{
+}

rules/S6922/python/metadata.json

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+{
+  "title": "arguments of \"tf.divide\" should have the same shape or be broadcastable to the same shape",
+  "type": "CODE_SMELL",
+  "status": "ready",
+  "remediation": {
+    "func": "Constant\/Issue",
+    "constantCost": "5min"
+  },
+  "tags": [
+  ],
+  "defaultSeverity": "Major",
+  "ruleSpecification": "RSPEC-6922",
+  "sqKey": "S6922",
+  "scope": "All",
+  "defaultQualityProfiles": ["Sonar way"],
+  "quickfix": "unknown",
+  "code": {
+    "impacts": {
+      "MAINTAINABILITY": "HIGH",
+      "RELIABILITY": "MEDIUM"
+    },
+    "attribute": "CONVENTIONAL"
+  }
+}

rules/S6922/python/rule.adoc

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+This rule raises an issue when the `tf.divide` function arguments have incompatible shapes.
+
+== Why is this an issue?
+
+The `tf.divide` function in TensorFlow is used to perform element-wise division between two tensors. It divides the corresponding elements of the first tensor by the corresponding elements of the second tensor, e.g.:
+
+[source,python]
+----
+x = tf.constant([4,6,8])
+y = tf.constant([2,3,4])
+----
+When you use `tf.divide(x, y)`, TensorFlow will perform element-wise division between `x` and `y`. So, the output would be:
+[source]
+----
+[4 / 2, 6 / 3, 8 / 4] = [2.0 2.0, 2.0]
+----
+Here, each element of `x` is divided by the corresponding element of `y`.
+
+One limitation to be aware of when using `tf.divide` is that both `x` and `y` must be of compatible shapes. In other words, they must have the same shape or be broadcastable to the same shape. Otherwise it will lead to `InvalidArgumentError`.
+
+== How to fix it
+Make sure that the `tf.divide` function arguments have compatible shapes.
+
+=== Code examples
+
+==== Noncompliant code example
+
+[source,python,diff-id=1,diff-type=noncompliant]
+----
+import tensorflow as tf
+
+x = tf.constant([4, 6, 8])
+y = tf.constant([2, 3])
+z = tf.divide(x, y)  # Noncompliant: InvalidArgumentError: Incompatible shapes: [3] vs. [2]
+----
+
+==== Compliant solution
+
+[source,python,diff-id=1,diff-type=compliant]
+----
+import tensorflow as tf
+
+x = tf.constant([4, 6, 8])
+y = tf.constant([2, 3, 4])
+z = tf.divide(x, y)  # OK: [2, 2, 2]
+----
+
+//=== How does this work?
+
+//=== Pitfalls
+
+//=== Going the extra mile
+
+
+== Resources
+=== Documentation
+* Tensorflow documentation - https://www.tensorflow.org/api_docs/python/tf/math/divide[tf.divide]
+* Numpy documentation - https://numpy.org/doc/stable/user/basics.broadcasting.html#broadcasting[Broadcasting]
+//=== Articles & blog posts
+//=== Conference presentations
+//=== Standards
+//=== External coding guidelines
+//=== Benchmarks