input: Write a python function to train and test a linear regression model using a dataset from a CSV file.
  assert train_test_linear_regression("Historical_Data.csv") is not None
output: def train_test_linear_regression(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 dataset
      data = pd.read_csv(file_path)
      
      # Assume 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 testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Initialize and train the linear regression model
      model = LinearRegression()
      model.fit(X_train, y_train)
      
      # Make predictions on the test set
      y_pred = model.predict(X_test)
      
      # Calculate the mean squared error
      mse = mean_squared_error(y_test, y_pred)
      
      return mse