input: Write a python function to load a CSV file and train a RandomForestClassifier model.
  assert isinstance(train_model('programming_languages.csv'), RandomForestClassifier)
  assert train_model('programming_languages.csv').n_estimators == 100
  assert train_model('programming_languages.csv').max_depth == 4
output: def train_model(csv_file):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      
      # Load the CSV file
      data = pd.read_csv(csv_file)
      
      # Assuming the last column is the target and the others are features
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      
      # 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=42)
      
      # Train the RandomForestClassifier model
      model = RandomForestClassifier(n_estimators=100, max_depth=4, random_state=42)
      model.fit(X_train, y_train)
      
      return model