input: Write a python function to load a CSV file, split the data into training and test sets, train a linear regression model, and compute the accuracy of the model.
  assert round(train_and_evaluate_model('product_x_data.csv'), 2) == 7.53  # Example accuracy value for illustration, replace with actual expected value
  assert round(train_and_evaluate_model('another_data.csv'), 2) == 4.21  # Example accuracy value for illustration, replace with actual expected value
output: def train_and_evaluate_model(csv_file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LinearRegression
      from sklearn.metrics import mean_squared_error
      
      # Load the CSV file
      data = pd.read_csv(csv_file_path)
      
      # Assuming the last column is the target variable and the rest are features
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      
      # Split the data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Train a linear regression model
      model = LinearRegression()
      model.fit(X_train, y_train)
      
      # Make predictions on the test set
      y_pred = model.predict(X_test)
      
      # Compute the mean squared error (MSE)
      mse = mean_squared_error(y_test, y_pred)
      
      return mse