input: Write a python function to load a CSV file using pandas and create a RandomForestClassifier model. The function should train the model on specified features and target, and return the trained model.
  assert isinstance(train_random_forest('wine.csv', ['alcohol', 'type', 'sulphates', 'pH', 'quality'], 'type'), RandomForestClassifier)
  assert train_random_forest('wine.csv', ['alcohol', 'type', 'sulphates', 'pH', 'quality'], 'type').n_estimators == 100
output: import pandas as pd
  from sklearn.ensemble import RandomForestClassifier
  
  def train_random_forest(csv_file, feature_cols, target_col):
      # Load the dataset
      df = pd.read_csv(csv_file)
      
      # Separate features and target
      X = df[feature_cols]
      y = df[target_col]
      
      # Initialize and train the RandomForestClassifier
      model = RandomForestClassifier(n_estimators=100, random_state=42)
      model.fit(X, y)
      
      # Return the trained model
      return model