def METHOD_NAME(active_worker_num):
    a_local_w = dict()
    a_local_w["linear.weight"] = torch.FloatTensor(
        [
            [-0.0003, 0.0192, -0.0294, 0.0219, 0.0037, 0.0021],
            [-0.0198, -0.0150, -0.0104, -0.0203, -0.0060, -0.0299],
            [-0.0201, 0.0149, -0.0333, -0.0203, 0.0012, 0.0080],
            [0.0237, 0.0103, -0.0219, 0.0088, -0.0009, 0.0009],
            [0.0144, -0.0336, -0.0346, -0.0222, -0.0025, -0.0138],
            [-0.0196, -0.0118, 0.0230, -0.0202, 0.0172, 0.0355],
        ]
    )
    a_local_w["linear.bias"] = torch.FloatTensor([-0.0753, -0.0589, -0.0907, -0.0672, 0.1159, 0.0290])
    b_local_w = dict()
    b_local_w["linear.weight"] = torch.FloatTensor(
        [
            [-0.0203, 0.0192, -0.0294, 0.0219, 0.0037, 0.0021],
            [-0.0198, -0.1150, -0.1104, -0.0203, -0.0060, -0.0299],
            [-0.0201, 0.0149, -0.0333, -0.0203, 0.0012, 0.0080],
            [0.0237, 0.0103, -0.0219, 0.0088, -0.1009, 0.0109],
            [0.0144, -0.0136, -0.0146, -0.0222, -0.0025, -0.0138],
            [-0.0196, -0.0118, 0.0230, -0.0202, 0.2172, 0.0355],
        ]
    )
    b_local_w["linear.bias"] = torch.FloatTensor([-0.1153, -0.0589, -0.1907, -0.0672, 0.1159, 0.0290])
    c_local_w = dict()
    c_local_w["linear.weight"] = torch.FloatTensor(
        [
            [-0.0003, 0.0192, -0.0294, 0.0219, 0.0037, 0.0021],
            [-0.0198, -0.1150, -0.0104, -0.0203, -0.0060, -0.0299],
            [-0.0201, 0.0149, -0.3333, -0.0203, 0.0012, 0.1180],
            [0.0237, 0.0103, -0.2219, 0.0088, -0.0009, 0.1009],
            [0.0144, -0.0336, -0.1346, -0.0222, -0.0025, -0.1138],
            [-0.0196, -0.0118, 0.0230, -0.2202, 0.0172, 0.1355],
        ]
    )
    c_local_w["linear.bias"] = torch.FloatTensor([-0.0753, -0.0189, -0.0207, -0.2672, 0.1159, 0.0290])
    model_list = []
    for i in range(active_worker_num):
        if i % 3 == 0:
            model_list.append((20 + i, copy.deepcopy(a_local_w)))  # add a random sample num
        if i % 3 == 1:
            model_list.append((30 + i, copy.deepcopy(b_local_w)))  # add a random sample num
        if i % 3 == 2:
            model_list.append((25 + i, copy.deepcopy(c_local_w)))  # add a random sample num
    return model_list