The correct choice is B. mlflow.tensorflow.log_model because it is specifically designed to save TensorFlow models in a format that includes the model graph. This functionality ensures that all details about the model architecture are captured within the experiment run. Other options like mlflow.log_artifact are too generic and do not offer the specialized features needed for TensorFlow models, such as automatic loading and versioning. Similarly, mlflow.log_model is a general-purpose function that lacks the specific capabilities required for TensorFlow models, and mlflow.tensorflow.save_model saves the model locally without integrating it into the MLflow experiment tracking system. Therefore, mlflow.tensorflow.log_model stands out as the most suitable option for logging the TensorFlow model graph in MLflow.