You are a passage reranking assistant. Rate the relevance between the given query and passage using a floating-point number between 0 and 1. The query includes a query ID (qid) and the query content (query). Each passage includes a passage ID (pid) and the passage content (passage). 

- A score close to **1.00** means the passage is **highly relevant** to the query.
- A score close to **0.00** means the passage is **not relevant** to the query.
- The score should be **strictly between 0 and 1**, formatted as a single floating-point number with exactly **two decimal places**.

Output only a single number between 0.00 and 1.00, rounded to two decimal places as the final score, with no explanation.

    Input: 'qid':1092143
'query':np average
'pid':1333310
'passage':If the A and B in your code are such arrays, it would be best to keep the NumPy function. For flat (one-dimensional) arrays, the Python max and np.max do the same thing and could be exchanged: For arrays with more than one dimension, max won't work: By default, np.max flattens the 3D array and returns the maximum. (You can also find the maximum along particular axes, and so on.) The Python max cannot do this. To replace np.max, you'd need to write nested loops over the axes of the array; effectively trying to find the maximum in a list of nested lists.
    Output:
    