text: Title: MissingConverter: Unable to find converter <class 'sklearn.preprocessing._encoders.OrdinalEncoder'>
  Body: ```
  ---------------------------------------------------------------------------
  MissingConverter                          Traceback (most recent call last)
  /var/folders/f2/9tbmpg411hndwc482xn850br0000gn/T/ipykernel_27005/3005074338.py in <module>
  ----> 1 hb_model = convert(clf, 'torch',X_train[0:1])
  
  ~/opt/anaconda3/lib/python3.9/site-packages/hummingbird/ml/convert.py in convert(model, backend, test_input, device, extra_config)
      442     """
      443     assert constants.REMAINDER_SIZE not in extra_config
  --> 444     return _convert_common(model, backend, test_input, device, extra_config)
      445 
      446 
  
  ~/opt/anaconda3/lib/python3.9/site-packages/hummingbird/ml/convert.py in _convert_common(model, backend, test_input, device, extra_config)
      403         return _convert_sparkml(model, backend_formatted, test_input, device, extra_config)
      404 
  --> 405     return _convert_sklearn(model, backend_formatted, test_input, device, extra_config)
      406 
      407 
  
  ~/opt/anaconda3/lib/python3.9/site-packages/hummingbird/ml/convert.py in _convert_sklearn(model, backend, test_input, device, extra_config)
      106     # We modify the scikit learn model during translation.
      107     model = deepcopy(model)
  --> 108     topology = parse_sklearn_api_model(model, extra_config)
      109 
      110     # Convert the Topology object into a PyTorch model.
  
  ~/opt/anaconda3/lib/python3.9/site-packages/hummingbird/ml/_parse.py in parse_sklearn_api_model(model, extra_config)
       63     # Parse the input scikit-learn model into a topology object.
       64     # Get the outputs of the model.
  ---> 65     outputs = _parse_sklearn_api(topology, model, inputs)
       66 
       67     # Declare output variables.
  
  ~/opt/anaconda3/lib/python3.9/site-packages/hummingbird/ml/_parse.py in _parse_sklearn_api(topology, model, inputs)
      228     tmodel = type(model)
      229     if tmodel in sklearn_api_parsers_map:
  --> 230         outputs = sklearn_api_parsers_map[tmodel](topology, model, inputs)
      231     else:
      232         outputs = _parse_sklearn_single_model(topology, model, inputs)
  
  ~/opt/anaconda3/lib/python3.9/site-packages/hummingbird/ml/_parse.py in _parse_sklearn_pipeline(topology, model, inputs)
      274     """
      275     for step in model.steps:
  --> 276         inputs = _parse_sklearn_api(topology, step[1], inputs)
      277     return inputs
      278 
  
  ~/opt/anaconda3/lib/python3.9/site-packages/hummingbird/ml/_parse.py in _parse_sklearn_api(topology, model, inputs)
      228     tmodel = type(model)
      229     if tmodel in sklearn_api_parsers_map:
  --> 230         outputs = sklearn_api_parsers_map[tmodel](topology, model, inputs)
      231     else:
      232         outputs = _parse_sklearn_single_model(topology, model, inputs)
  
  ~/opt/anaconda3/lib/python3.9/site-packages/hummingbird/ml/_parse.py in _parse_sklearn_column_transformer(topology, model, inputs)
      451                 )
      452         else:
  --> 453             var_out = _parse_sklearn_api(topology, model_obj, transform_inputs)[0]
      454             if model.transformer_weights is not None and name in model.transformer_weights:
      455                 # Create a Multiply node
  
  ~/opt/anaconda3/lib/python3.9/site-packages/hummingbird/ml/_parse.py in _parse_sklearn_api(topology, model, inputs)
      230         outputs = sklearn_api_parsers_map[tmodel](topology, model, inputs)
      231     else:
  --> 232         outputs = _parse_sklearn_single_model(topology, model, inputs)
      233 
      234     return outputs
  
  ~/opt/anaconda3/lib/python3.9/site-packages/hummingbird/ml/_parse.py in _parse_sklearn_single_model(topology, model, inputs)
      250         raise RuntimeError("Parameter model must be an object not a " "string '{0}'.".format(model))
      251 
  --> 252     alias = get_sklearn_api_operator_name(type(model))
      253     this_operator = topology.declare_logical_operator(alias, model)
      254     this_operator.inputs = inputs
  
  ~/opt/anaconda3/lib/python3.9/site-packages/hummingbird/ml/supported.py in get_sklearn_api_operator_name(model_type)
      463     """
      464     if model_type not in sklearn_api_operator_name_map:
  --> 465         raise MissingConverter("Unable to find converter for model type {}.".format(model_type))
      466     return sklearn_api_operator_name_map[model_type]
      467 
  
  MissingConverter: Unable to find a converter for model type <class 'sklearn.preprocessing._encoders.OrdinalEncoder'>.
  It usually means the pipeline being converted contains a
  transformer or a predictor with no corresponding converter implemented.
  Please fill an issue at https://github.com/microsoft/hummingbird.
  ```
  
  **Which Scikit-learn pipeline operators do hummingbird support?** 