[BUG] Error when using custom Transformer with TabularSHAP in SynapseML

### SynapseML version

0.10.1

### System information

- **Language version** (e.g. python 3.8, scala 2.12):
python 3.10
- **Spark Version** (e.g. 3.2.3):
- 3.3.1
- **Spark Platform** (e.g. Synapse, Databricks)
- Amazon EKS

### Describe the problem

Hello,

I encountered an issue when using the TabularSHAP module in SynapseML with a custom Transformer.I received the following error message (`AttributeError: `SimpleTransformer` object has no attribute '_to_java'`).

I believe this issue may be caused by either a bug in the TabularSHAP implementation or an insufficient implementation of my custom Transformer.
Could you please help me determine whether this issue is due to a bug in TabularSHAP or a problem with my custom Transformer.If it is the latter, any suggestions for improving my implementation would be greatly appreciated.

Thank you in advice for your assistance.

### Code to reproduce issue

```
class SimpleTransformer(
Transformer,
HasInputCol,
HasOutputCol,
DefaultParamsReadable,
DefaultParamsWritable,
):
inputCol = Param(
Params._dummy(),
"inputCol",
"inputCol",
)
outputCol = Param(
Params._dummy(),
"outputCol",
"outputCol",
)
num = Param(
Params._dummy(),
"num",
"the Number of putting value",
)

@keyword_only
def __init__(self, inputCol=None, outputCol=None, num=0):
super().__init__()
self._setDefault(num=0)
kwargs = self._input_kwargs
self.setParams(**kwargs)

@keyword_only
def setParams(self, inputCol=None, outputCol=None, num=0):
kwargs = self._input_kwargs
self._set(**kwargs)

def getNum(self):
return self.getOrDefault(self.num)

def _transform(self, dataset):
if not self.isSet("inputCol"):
raise ValueError("No")

input_columns = self.getInputCol()
output_column = self.getOutputCol()
num = self.getNum()

return dataset.withColumn(output_column, F.col(input_columns) + num)

sdf = spark.createDataFrame(
[
[
'iD-01',
1,
1,
'a',
4,
],
[
'iD-02',
2,
2,
'b',
3,
],
[
'iD-03',
3,
3,
'c',
4,
],
[
'iD-04',
0,
0,
'b',
1,
],
*[
[
f'iD-SAMPLE{i}-label1',
1,
1,
'a',
4,
]
for i in range(100)
],
*[
[
f'iD-SAMPLE{i}-label2',
2,
2,
'b',
3,
]
for i in range(100)
],
*[
[
f'iD-SAMPLE{i}-label3',
3,
3,
'c',
4,
]
for i in range(100)
],
*[
[
f'iD-SAMPLE{i}-label0',
0,
0,
'b',
1,
]
for i in range(100)
],
],
schema=['ID', 'colA', 'colB', 'colC', 'colD'],
)

si = StringIndexer(inputCol='colC', outputCol='featured_colC')
st = SimpleTransformer(inputCol="colB", outputCol="newColB", num=1)
va = VectorAssembler(
inputCols=['newColB', 'featured_colC', 'colD'], outputCol='features'
)

model = LightGBMClassifier(
objective="multiclass",
featuresCol="features",
labelCol="colA",
numTasks=3,
useBarrierExecutionMode=True,
categoricalSlotIndexes=[1],
categoricalSlotNames=['featured_colC'],
)

pipeline = Pipeline(stages=[si, st, va, model])
model = pipeline.fit(sdf)

explain_instances = model.transform(sdf)

from pyspark.sql.functions import broadcast, rand
from synapse.ml.explainers import TabularSHAP

shap = TabularSHAP(
inputCols=["colB", "colC", "colD"],
outputCol="shapValues",
numSamples=5000,
model=model,
targetCol="probability",
targetClasses=[1, 2, 3],
backgroundData=broadcast(sdf.orderBy(rand()).limit(100).cache()),
)

# We got some errors, "Attribution Error: `SimpleTransfomer` object has no attribute '_to_java'"
shap_df = shap.transform(explain_instances)
```

### Other info / logs

Attribution Error: `SimpleTransfomer` object has no attribute '_to_java'

### What component(s) does this bug affect?

- [ ] `area/cognitive`: Cognitive project
- [X] `area/core`: Core project
- [ ] `area/deep-learning`: DeepLearning project
- [X] `area/lightgbm`: Lightgbm project
- [ ] `area/opencv`: Opencv project
- [ ] `area/vw`: VW project
- [ ] `area/website`: Website
- [ ] `area/build`: Project build system
- [ ] `area/notebooks`: Samples under notebooks folder
- [ ] `area/docker`: Docker usage
- [ ] `area/models`: models related issue

### What language(s) does this bug affect?

- [ ] `language/scala`: Scala source code
- [ ] `language/python`: Pyspark APIs
- [ ] `language/r`: R APIs
- [ ] `language/csharp`: .NET APIs
- [ ] `language/new`: Proposals for new client languages

### What integration(s) does this bug affect?

- [ ] `integrations/synapse`: Azure Synapse integrations
- [ ] `integrations/azureml`: Azure ML integrations
- [ ] `integrations/databricks`: Databricks integrations