text: Title: SparkML OneHotEncoder conversion is wrong
  Body: SparkML OneHotEncoder conversion is implemented wrong. It just so happens to handle the conversion when there is one input column and one output column. Once you try multiple inputCols and outputCols, the generated ONNX graph is wrong.
  
  Repro:
  
  ```python
  
  from pyspark.ml.feature import OneHotEncoder
  from onnxconverter_common.data_types import FloatTensorType, Int32TensorType
  from onnxmltools.convert import convert_sparkml
  from onnxruntime import InferenceSession
  
  df = spark.createDataFrame(
    [(0, 2), (1, 4),],
    ["col1", "col2"]
  )
  
  encoder = OneHotEncoder(inputCols = ["col1", "col2"], outputCols = ["col1_enc", "col2_enc"], dropLast=False).fit(df)
  
  initial_types = [
    ("col1", Int32TensorType([None, 1])),
    ("col2", Int32TensorType([None, 1])),
  ]
  
  onnx_model = convert_sparkml(encoder, 'OneHotEncoder', initial_types)
  
  # Writing the model out for inspection
  with open("ohe_quicktest.onnx", "wb") as f:
      f.write(onnx_model.SerializeToString())
  
  # Run with onnx runtime
  input_data = {"col1": [(0, 1)], "col2": [(2, 4)]}
  session = InferenceSession(onnx_model.SerializeToString(), providers=["CPUExecutionProvider"])
  output = session.run(None, input_data)
  ```
  
  Expected result:
  The model can correct encode the input features.
  
  Actual result:
  InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Failed to load model with error: /onnxruntime_src/onnxruntime/core/graph/graph.cc:1368 void onnxruntime::Graph::InitializeStateFromModelFileGraphProto() This is an invalid model. Graph output (col2_enc) does not exist in the graph.
  
  The generated ONNX graph looks like this, which is obviously wrong:
  ![image](https://user-images.githubusercontent.com/6992432/169629737-2fe2a869-d4e0-484c-ab1b-6c3fe875d513.png)
  