input: Write a python function to train a RandomForestClassifier on a given dataset and make predictions.
  assert train_and_predict(<path_to_csv>) == y_pred # assuming y_pred is a known expected result
  assert train_and_predict(<another_path_to_csv>) == another_y_pred # assuming another_y_pred is a known expected result
  assert train_and_predict(<yet_another_path_to_csv>) == yet_another_y_pred # assuming yet_another_y_pred is a known expected result
output: def train_and_predict(csv_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
      
      # Load the dataset
      data = pd.read_csv(csv_path)
      
      # Assuming the last column is the target variable
      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)
      
      # Initialize the RandomForestClassifier
      clf = RandomForestClassifier(random_state=42)
      
      # Train the classifier
      clf.fit(X_train, y_train)
      
      # Make predictions
      y_pred = clf.predict(X_test)
      
      return y_pred