approved suggestion

dan/predict/attention.py

@@ -44,8 +44,7 @@ def compute_coverage(text: str, max_value: float, offset: int, attentions):
     # blank vector to accumulate weights for the current text
     coverage_vector = np.zeros((height, width))
     for i in range(len(text)):
-        local_weight = attentions[i + offset]
-        local_weight = cv2.resize(local_weight, (width, height))
+        local_weight = cv2.resize(attentions[i + offset], (width, height))
         coverage_vector = np.clip(coverage_vector + local_weight, 0, 1)
 
     # Normalize coverage vector