input: Write a python function to load a CSV file into a pandas DataFrame, split it into features and labels, train a RandomForestClassifier, and make predictions on the dataset.
  assert len(load_and_predict('dataset.csv')) == len(pd.read_csv('dataset.csv'))
  assert isinstance(load_and_predict('dataset.csv')[0], int)
  assert isinstance(load_and_predict('dataset.csv'), np.ndarray)
output: import pandas as pd
  import numpy as np
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  
  def load_and_predict(file_path):
      # Load CSV file into a pandas DataFrame
      df = pd.read_csv(file_path)
      
      # Assuming the last column is the label
      X = df.iloc[:, :-1]
      y = df.iloc[:, -1]
      
      # 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 a RandomForestClassifier
      clf = RandomForestClassifier(n_estimators=100, random_state=42)
      clf.fit(X_train, y_train)
      
      # Make predictions on the dataset
      predictions = clf.predict(X)
      
      return predictions