input: Write a python function to load data from a CSV file, extract features and labels, build an ANN model, train it, and return the trained model.
  assert isinstance(train_ann_model('data.csv'), tf.keras.Sequential)
  assert hasattr(train_ann_model('data.csv'), 'fit')
  assert hasattr(train_ann_model('data.csv'), 'predict')
output: def train_ann_model(csv_file):
      import pandas as pd
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Load data from CSV file
      data = pd.read_csv(csv_file)
  
      # Assuming the last column is the label
      features = data.iloc[:, :-1].values
      labels = data.iloc[:, -1].values
  
      # Build the ANN model
      model = Sequential([
          Dense(64, activation='relu', input_shape=(features.shape[1],)),
          Dense(64, activation='relu'),
          Dense(1, activation='sigmoid') # Assuming binary classification
      ])
  
      # Compile the model
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
      # Train the model
      model.fit(features, labels, epochs=10, batch_size=32, verbose=0)
  
      return model