input: Write a python function to load a CSV file, split the data into features and labels, create a linear regression model, train the model, make predictions, and print the R^2 score.
  ```python
  assert linear_regression_pipeline("data.csv") is not None
  assert linear_regression_pipeline("data.csv") >= 0 and linear_regression_pipeline("data.csv") <= 1
output: def linear_regression_pipeline(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LinearRegression
      from sklearn.metrics import r2_score
  
      # Load the data
      data = pd.read_csv(file_path)
  
      # Split the data into features and labels
      X = data.iloc[:, :-1]  # assuming the last column is the label
      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)
  
      # Create the linear regression model
      model = LinearRegression()
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Make predictions
      y_pred = model.predict(X_test)
  
      # Calculate and print the R^2 score
      r2 = r2_score(y_test, y_pred)
      print(f"R^2 score: {r2}")
  
      return r2