input: Write a python function to load a dataset, create target and feature variables, and train a decision tree classifier.
  assert isinstance(load_and_train("data.csv"), tree.DecisionTreeClassifier)
output: def load_and_train(file_path):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.model_selection import train_test_split
      
      # Load the dataset
      data = pd.read_csv(file_path)
      
      # Assuming the last column is the target variable
      X = data.iloc[:, :-1]  # Features
      y = data.iloc[:, -1]   # Target
      
      # Split the dataset into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Initialize the DecisionTreeClassifier
      clf = DecisionTreeClassifier()
      
      # Train the classifier
      clf.fit(X_train, y_train)
      
      return clf