chore: add test for #28

chapi-ast-python/src/test/kotlin/chapi/ast/pythonast/PythonFullIdentListenerTest.kt

@@ -230,6 +230,65 @@ class Employee:
         self.name = name
  
 emp = Employee("Zara")
+"""
+        PythonAnalyser().analysis(code, "")
+    }
+
+    @Test
+    internal fun shouldHandleForImportError() {
+        val code = """
+from dsp.utils import normalize_text
+from dspy.primitives.prediction import Completions, Prediction
+
+default_normalize = lambda s: normalize_text(s) or None
+
+
+def majority(prediction_or_completions, normalize=default_normalize, field=None):
+    ""${'"'}
+        Returns the most common completion for the target field (or the last field) in the signature.
+        When normalize returns None, that completion is ignored.
+        In case of a tie, earlier completion are prioritized.
+    ""${'"'}
+
+    assert any(isinstance(prediction_or_completions, t) for t in [Prediction, Completions, list])
+    input_type = type(prediction_or_completions)
+
+    # Get the completions
+    if isinstance(prediction_or_completions, Prediction):
+        completions = prediction_or_completions.completions
+    else:
+        completions = prediction_or_completions
+    
+    try:
+        signature = completions.signature
+    except:
+        signature = None
+    
+    if not field:
+        if signature:
+            field = signature.output_fields[-1]
+        else:
+            field = list(completions[0].keys())[-1]
+
+    # Normalize
+    normalize = normalize if normalize else lambda x: x
+    normalized_values = [normalize(completion[field]) for completion in completions]
+    normalized_values_ = [x for x in normalized_values if x is not None]
+    
+    # Count
+    value_counts = {}
+    for value in (normalized_values_ or normalized_values):
+        value_counts[value] = value_counts.get(value, 0) + 1
+
+    majority_value = max(value_counts, key=value_counts.get)
+
+    # Return the first completion with the majority value in the field
+    for completion in completions:
+        if normalize(completion[field]) == majority_value:
+            break
+    
+    # if input_type == Prediction:
+    return Prediction.from_completions([completion], signature=signature)
 """
         PythonAnalyser().analysis(code, "")
     }