input: Write a python function to load data from a CSV file and train a decision tree classifier on it using scikit-learn.
  assert type(load_and_train("data.csv")) == tree.DecisionTreeClassifier
  assert load_and_train("test.csv").predict([[5.1, 3.5, 1.4, 0.2]]) in [0, 1, 2]
  assert load_and_train("sample.csv").predict([[6.2, 3.4, 5.4, 2.3]]) in [0, 1, 2]
output: def load_and_train(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      from sklearn import tree
      
      # Load the data from the CSV file
      data = pd.read_csv(file_path)
      
      # Assume the last column is the target variable
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      
      # Split the data into training and testing sets (although we might not need testing for this task)
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Initialize and train the Decision Tree Classifier
      clf = DecisionTreeClassifier()
      clf.fit(X_train, y_train)
      
      # Return the trained classifier
      return clf