input: Write a python function to read a CSV file, perform basic data cleaning by dropping rows with NaN values, convert categorical variables 'country' and 'gender' to dummy variables, scale the numeric variables 'age' and 'height' using MinMaxScaler, and save the optimized dataset to a new CSV file.
  assert process_dataset('dataset.csv', 'optimized_dataset.csv') == 'optimized_dataset.csv'
output: def process_dataset(input_file, output_file):
      import pandas as pd
      from sklearn.preprocessing import MinMaxScaler
  
      # Read the CSV file
      df = pd.read_csv(input_file)
      
      # Drop rows with NaN values
      df = df.dropna()
      
      # Convert categorical variables to dummy variables
      df = pd.get_dummies(df, columns=['country', 'gender'])
      
      # Scale numeric variables
      scaler = MinMaxScaler()
      df[['age', 'height']] = scaler.fit_transform(df[['age', 'height']])
      
      # Save the optimized dataset to a new CSV file
      df.to_csv(output_file, index=False)
      
      return output_file