# model.py.txt

# model.py
class Model:
    def __init__(self):
        # Initialize the model and any required components
        pass

    def train(self, train_data):
        # Train the model using the provided training data
        # Implement model training functionality here
        pass

    def predict(self, input_data):
        # Make predictions using the trained model
        # Implement model prediction functionality here
        pass

    def evaluate(self, eval_data):
        # Evaluate the model's performance on the provided evaluation data
        # Implement model evaluation functionality here
        pass


# run.py
from model import Model

class TaskRunner:
    def __init__(self, model):
        self.model = model

    def load_dataset(self, dataset_name):
        # Load the CodeXGLUE dataset into memory
        # Implement dataset loading functionality here
        pass

    def preprocess(self):
        # Preprocess and clean the loaded dataset
        # Implement data preprocessing functionality here
        pass

    def eval(self, task_name, output_path):
        # Evaluate the model's performance on a specific task
        # Implement model evaluation functionality here
        pass

    def baseline(self, task_name):
        # Compare the model's performance against the baseline model
        # Implement baseline comparison functionality here
        pass

    def train_model(self, train_data):
        # Train the model using the provided training data
        self.model.train(train_data)

    def predict_model(self, input_data):
        # Make predictions using the trained model
        return self.model.predict(input_data)

    def evaluate_model(self, eval_data):
        # Evaluate the model's performance on the provided evaluation data
        return self.model.evaluate(eval_data)

    def run(self, task_name):
        # Run a specific task on the model
        self.load_dataset(task_name)
        self.preprocess()
        self.evaluate_model(task_name)
        self.baseline(task_name)
        # Implement additional task-specific functionality here


model = Model()
task_runner = TaskRunner(model)

# Example usage
train_data = ...
input_data = ...
eval_data = ...

task_runner.train_model(train_data)
predictions = task_runner.predict_model(input_data)
evaluation_result = task_runner.evaluate_model(eval_data)

task_runner.run('task_name')