input: Write a python function to load a CSV file, split it into training and testing sets, apply feature scaling, and train a logistic regression model.
  assert isinstance(load_and_train('CustomerChurn.csv'), LogisticRegression)
  assert load_and_train('CustomerChurn.csv').random_state == 0
output: import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import StandardScaler
  from sklearn.linear_model import LogisticRegression
  
  def load_and_train(csv_file):
      # Load the CSV file into a DataFrame
      data = pd.read_csv(csv_file)
      
      # Assuming the last column is the target variable and the rest are features
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
      
      # Apply feature scaling
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
      
      # Train the logistic regression model
      model = LogisticRegression(random_state=0)
      model.fit(X_train, y_train)
      
      return model