compress normalization func

README.md

@@ -25240,8 +25240,7 @@ outputs = session.run(None, inputs)[0]
 
 # Apply mean pooling to 'outputs' to get a single representation of each text
 embeddings = mean_pooling(outputs, input_text["attention_mask"])
-norm = np.linalg.norm(embeddings, ord=2, axis=1, keepdims=True)
-embeddings = embeddings / norm
+embeddings = embeddings / np.linalg.norm(embeddings, ord=2, axis=1, keepdims=True)
 ```
 
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