input: def classify_mnist_digits(dataset: str) -> LogisticRegression:
      """ Generates a machine learning model using Logistic Regression for classifying 
      digits in the MNIST dataset. The dataset is loaded, features and labels are 
      separated, and the model is fitted to the data.
      >>> classify_mnist_digits('mnist')
      call of the function consists of data loading, instantiating, and fitting the model: LogisticRegression(...), data: (70000, 784), (70000,)
      """
output:     from sklearn.datasets import fetch_openml
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      from sklearn.preprocessing import StandardScaler
  
      # Load the MNIST dataset
      mnist = fetch_openml(dataset, version=1, as_frame=False)
      X, y = mnist["data"], mnist["target"]
  
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Standardize the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Instantiate and fit the Logistic Regression model
      model = LogisticRegression(max_iter=1000)
      model.fit(X_train, y_train)
  
      return model