input: Write a python function to train a simple neural network model using TensorFlow and evaluate it on test data.
  assert len(train_model(pd.DataFrame({'Open': [10, 20, 30], 'Close': [15, 25, 35]}), 10)) == 10
  assert train_model(pd.DataFrame({'Open': [1, 2, 3], 'Close': [2, 4, 6]}), 5)[0] > 0
  assert isinstance(train_model(pd.DataFrame({'Open': [5, 10, 15], 'Close': [10, 20, 30]}), 10), np.ndarray)
output: import tensorflow as tf
  import numpy as np
  import pandas as pd
  
  def train_model(data, epochs):
      # Prepare the data
      X = data[['Open']].values
      y = data['Close'].values
  
      # Define the model
      model = tf.keras.models.Sequential([
          tf.keras.layers.Dense(10, input_dim=1, activation='relu'),
          tf.keras.layers.Dense(1)
      ])
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X, y, epochs=epochs, verbose=0)
  
      # Evaluate the model on the same data (since no test data is provided)
      predictions = model.predict(X)
  
      return predictions.flatten()