import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

class MyModel:
    def __init__(self):
        # Initialize the model
        self.model = Sequential([
            Dense(16, activation='relu', input_shape=(10,)),  # Adjust input_shape as needed
            Dense(8, activation='relu'),
            Dense(4, activation='relu'),
            Dense(2, activation='relu'),
            Dense(1)
        ])
        self.model.compile(loss='mse', optimizer='adam', metrics=[tf.keras.metrics.MeanSquaredError()])

    def load_model(self, path):
        # Load the model weights
        self.model.load_weights(path)

    def predict(self, input_data):
        # Make predictions
        input_data = np.array(input_data)
        return self.model.predict(input_data).tolist()