input: Write a python function to split a dataset into training and test sets, normalize the data, train a linear regression model, and return the model's accuracy.
  assert round(train_and_evaluate_model('dataset1.csv', test_size=0.2), 2) == 0.85
  assert round(train_and_evaluate_model('dataset2.csv', test_size=0.3), 2) == 0.90
  assert round(train_and_evaluate_model('dataset3.csv', test_size=0.25), 2) == 0.88
output: def train_and_evaluate_model(file_path, test_size=0.2):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from sklearn.linear_model import LinearRegression
      from sklearn.metrics import r2_score
  
      # Load the dataset
      data = pd.read_csv(file_path)
  
      # Assuming the last column is the target and the rest are features
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # Split the dataset into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
  
      # Normalize the data
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Train the linear regression model
      model = LinearRegression()
      model.fit(X_train, y_train)
  
      # Make predictions and calculate accuracy
      y_pred = model.predict(X_test)
      accuracy = r2_score(y_test, y_pred)
  
      return accuracy