Project: 014_Energy-based_Backdoor_Defense_Against_Federated_Gr | Function: select_models_based_on_energy (iclr)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** TECC (Topological Energy Client Clustering) method that clusters clients based on energy patterns to identify and filter malicious participants

**KEY TERMS:** {
  "client_energies": "Ek energy elements for each client",
  "padded_energies": "normalized energy distributions",
  "FINCH": "FINCH clustering algorithm",
  "labels": "cluster assignment labels",
  "malicious_cluster": "cluster with highest energy (malicious)",
  "selected_models_index": "indices of benign clients"
}

**CONTEXT CODE:** import numpy as np
import torch
try:
    from finch import FINCH
except ImportError:
    # Mock FINCH for testing
    def FINCH(data):
        n_samples = len(data)
        # Simple mock: create 2 clusters based on energy magnitude
        labels = np.array([0 if np.mean(energy) < 0.5 else 1 for energy in data])
        c = labels.reshape(-1, 1)
        num_clust = np.array([len(np.unique(labels))])
        return c, num_clust, None
from scipy.spatial.distance import jensenshannon
from scipy.stats import ks_2samp
from random import sample

def select_models_based_on_energy(client_energies):
    """
    Cluster the models based on energy differences to identify malicious clients.
    args:
    - client_energies: List of energy differences for each client
    Return:
    - selected_models_index: List of selected model indices
    """
    raise NotImplementedError("Function implementation has been masked for testing")

Complete the function implementation replacing "raise NotImplementedError".