input: 
  def classify_digits(dataset: str) -> LogisticRegression:
      """
      Generates a machine learning model using scikit-learn for classifying digits in the provided dataset.
      Assuming the dataset is a preprocessed form like MNIST. 
      The function will split the dataset into features and labels, reshape it, and fit a Logistic Regression model on it.
      >>> model = classify_digits('mnist_784')
      >>> model.predict(X[0:1])
      array['5']
      """
  
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
      from sklearn.pipeline import make_pipeline
  
      # Load the dataset
      data = fetch_openml(name=dataset, version=1, as_frame=False)
      X, y = data.data, data.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)
  
      # Create a pipeline with StandardScaler and LogisticRegression
      model = make_pipeline(StandardScaler(), LogisticRegression(max_iter=200))
  
      # Fit the model on the training data
      model.fit(X_train, y_train)
  
      return model