text: Title: [BUG] interpretable predictor interpretable_models_summary print_interpretable_rules not available
  Body: **Bug Report Checklist**
  <!-- Please ensure at least one of the following to help the developers troubleshoot the problem:  -->
  - [x] I provided code that demonstrates a minimal reproducible example. <!-- Ideal, especially via source install --> 
  - [ ] I confirmed bug exists on the latest mainline of AutoGluon via source install. <!-- Preferred --> 
  - [x] I confirmed bug exists on the latest stable version of AutoGluon. <!-- Unnecessary if prior items are checked --> 
  
  **Describe the bug**
  Following usage example from documentation (of version 0.5.1?) training works but no summary and no interpretable rules
  https://auto.gluon.ai/0.5.1/tutorials/tabular_prediction/tabular-interpretability.html
  
  **Expected behavior**
  as described in documentation 
  
  **To Reproduce**
  <!-- A minimal script to reproduce the issue. Links to Colab notebooks or similar tools are encouraged.  
  If the code is too long, feel free to put it in a public gist and link it in the issue: https://gist.github.com.  
  In short, we are going to copy-paste your code to run it and we expect to get the same result as you. -->
  ```
  from autogluon.tabular import TabularDataset, TabularPredictor
  train_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')
  subsample_size = 500  # subsample subset of data for faster demo, try setting this to much larger values
  train_data = train_data.sample(n=subsample_size, random_state=0)
  train_data.head()
  predictor = TabularPredictor(label='class')
  predictor.fit(train_data, presets='interpretable')
  predictor.leaderboard()
  predictor.interpretable_models_summary()
  predictor.print_interpretable_rules() # can optionally specify a model name or complexity threshold
  ```
  
  **Screenshots / Logs**
  ---------------------------------------------------------------------------
  AttributeError                            Traceback (most recent call last)
  Cell In[3], line 1
  ----> 1 predictor.interpretable_models_summary()
  
  AttributeError: 'TabularPredictor' object has no attribute 'interpretable_models_summary'
  
  
  ---------------------------------------------------------------------------
  AttributeError                            Traceback (most recent call last)
  Cell In[4], line 1
  ----> 1 predictor.print_interpretable_rules()
  
  AttributeError: 'TabularPredictor' object has no attribute 'print_interpretable_rules'
  
  
  **Installed Versions**
  <!-- Please run the following code snippet: -->
  <details>
  
  ```python
  INSTALLED VERSIONS
  ------------------
  date                   : 2024-01-14
  time                   : 12:06:25.974741
  python                 : 3.10.12.final.0
  OS                     : Linux
  OS-release             : 6.2.0-1017-aws
  Version                : #17~22.04.1-Ubuntu SMP Fri Nov 17 21:07:13 UTC 2023
  machine                : x86_64
  processor              : x86_64
  num_cores              : 128
  cpu_ram_mb             : 253829.41015625
  cuda version           : None
  num_gpus               : 0
  gpu_ram_mb             : []
  avail_disk_size_mb     : 98683
  
  accelerate             : 0.21.0
  async-timeout          : 4.0.3
  autogluon              : 1.0.0
  autogluon.common       : 1.0.0
  autogluon.core         : 1.0.0
  autogluon.features     : 1.0.0
  autogluon.multimodal   : 1.0.0
  autogluon.tabular      : 1.0.0
  autogluon.timeseries   : 1.0.0
  boto3                  : 1.34.16
  catboost               : 1.2.2
  defusedxml             : 0.7.1
  evaluate               : 0.4.1
  fastai                 : 2.7.13
  gluonts                : 0.14.3
  hyperopt               : 0.2.7
  imodels                : 1.4.1
  jinja2                 : 3.0.3
  joblib                 : 1.3.2
  jsonschema             : 4.20.0
  lightgbm               : 4.1.0
  lightning              : 2.0.9.post0
  matplotlib             : 3.8.2
  mlforecast             : 0.10.0
  networkx               : 3.2.1
  nlpaug                 : 1.1.11
  nltk                   : 3.8.1
  nptyping               : 2.4.1
  numpy                  : 1.26.3
  nvidia-ml-py3          : 7.352.0
  omegaconf              : 2.2.3
  onnxruntime-gpu        : None
  openmim                : 0.3.9
  orjson                 : 3.9.10
  pandas                 : 2.1.4
  Pillow                 : 10.2.0
  psutil                 : 5.9.7
  PyMuPDF                : None
  pytesseract            : 0.3.10
  pytorch-lightning      : 2.0.9.post0
  pytorch-metric-learning: 1.7.3
  ray                    : 2.6.3
  requests               : 2.31.0
  scikit-image           : 0.20.0
  scikit-learn           : 1.3.2
  scikit-learn-intelex   : None
  scipy                  : 1.11.4
  seqeval                : 1.2.2
  setuptools             : 60.2.0
  skl2onnx               : None
  statsforecast          : 1.4.0
  statsmodels            : 0.14.1
  tabpfn                 : None
  tensorboard            : 2.15.1
  text-unidecode         : 1.3
  timm                   : 0.9.12
  torch                  : 2.0.1
  torchmetrics           : 1.1.2
  torchvision            : 0.15.2
  tqdm                   : 4.65.2
  transformers           : 4.31.0
  utilsforecast          : 0.0.10
  vowpalwabbit           : None
  xgboost                : 2.0.3
  
  
  ```
  
  </details>
  