input: Write a python function to train a neural network to classify flowers using sklearn and evaluate its accuracy.
  assert abs(train_and_evaluate_nn('flowers.csv') - 1.00) < 0.05  # Assuming near perfect accuracy for simplicity
  assert isinstance(train_and_evaluate_nn('flowers.csv'), float)
output: def train_and_evaluate_nn(csv_file):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from sklearn.neural_network import MLPClassifier
      from sklearn.metrics import accuracy_score
  
      # Load the dataset
      data = pd.read_csv(csv_file)
  
      # Assuming the last column is the target variable and rest are features
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
  
      # Standardize the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Initialize and train the neural network
      model = MLPClassifier(hidden_layer_sizes=(100,), max_iter=300, random_state=42)
      model.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = model.predict(X_test)
  
      # Calculate the accuracy of the model
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy