input: Write a function to split a dataset into training and test sets, scale the data, and build a sequential neural network model using Keras.
  assert build_and_train_model("wine.csv") is not None
output: import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import StandardScaler
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense
  
  def build_and_train_model(dataset_path):
      # Load dataset
      data = pd.read_csv(dataset_path)
      
      # Assume the last column is the target
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      
      # Split the data 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)
      
      # Scale the data
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
      
      # Build a sequential neural network model
      model = Sequential()
      model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='sigmoid'))  # assuming a binary classification
      
      # Compile the model
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      
      # Train the model
      model.fit(X_train, y_train, epochs=50, batch_size=10, validation_data=(X_test, y_test))
      
      return model