input: Write a python function to train a linear regression model and return the mean squared error, intercept, and coefficients.
  assert train_and_evaluate('dataset.csv')[0] > 0
  assert isinstance(train_and_evaluate('dataset.csv')[1], float)
  assert isinstance(train_and_evaluate('dataset.csv')[2], np.ndarray)
output: def train_and_evaluate(file_path):
      import pandas as pd
      import numpy as np
      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 variable and the rest are features
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      
      # 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)
      
      # Train the Linear Regression model
      model = LinearRegression()
      model.fit(X_train, y_train)
      
      # Predict on the testing set
      y_pred = model.predict(X_test)
      
      # Calculate the mean squared error
      mse = mean_squared_error(y_test, y_pred)
      
      # Get the intercept and coefficients
      intercept = model.intercept_
      coefficients = model.coef_
      
      return mse, intercept, coefficients