input: Write a function to fit a DecisionTreeClassifier model on a given dataset using 'entropy' criterion.
  assert fit_decision_tree_model('data.csv') is not None
  assert hasattr(fit_decision_tree_model('data.csv'), 'predict')
  assert fit_decision_tree_model('data.csv').criterion == 'entropy'
output: def fit_decision_tree_model(file_path):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      
      # Load the dataset
      data = pd.read_csv(file_path)
      
      # Assuming the last column is the target variable
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      
      # Initialize the DecisionTreeClassifier with 'entropy' criterion
      model = DecisionTreeClassifier(criterion='entropy')
      
      # Fit the model
      model.fit(X, y)
      
      return model