Invalid prediction values for multi-class decision tree ensembles converted from scikit-learn

## 🐞Describing the bug

Multiclass decision tree ensembles (i.e. Random Forest) converted from `sklearn` don't produce predictions matching those obtained from `predict_proba()`. It appears that the resulting probabilities are not scaled by the number of trees in the ensemble.

## To Reproduce
```py
from sklearn.cross_validation import train_test_split
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
import coremltools as ct

N_TREES = 13
N_CLASSES = 3

X, y = make_classification(
n_samples=1000,
n_features=8,
n_informative=4,
n_redundant=0,
n_classes=N_CLASSES,
random_state=0,
shuffle=True,
)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.33, random_state=0
)

clf = RandomForestClassifier(max_depth=2, random_state=0, n_estimators=N_TREES)
clf.fit(X_train, y_train)

coreml_model = ct.converters.sklearn.convert(clf, "input")
sk_results = clf.predict_proba(X_test)
for index, row in enumerate(X_test):
coreml_result = coreml_model.predict({"input": row})
sk_result = sk_results[index]
for class_i in range(N_CLASSES):
assert (
abs(
coreml_result["classProbability"][class_i]
- N_TREES * sk_result[class_i]
)
< 1e-7
)
```

## System environment
- coremltools version: 5.2.0
- scikit-learn: 0.19.2
- OS (e.g. MacOS version or Linux type): macOS 12.3.1

## Additional context

It looks like the `scaling` parameter is not used for multi class decision trees, but IS used for two-class decision trees. Compare line 39 to line 28 in `_tree_ensemble.py`:

https://github.com/apple/coremltools/blob/aeb3e8e50dc3af06a8a8988fedcb931c4344dfa3/coremltools/converters/sklearn/_tree_ensemble.py#L25-L43

Start with the reproduction script and inspect coremltools/converters/sklearn/_tree_ensemble.py, especially the linked lines 25–43 where scaling differs between two-class and multi-class trees. Verify the converted model's classProbability values against scikit-learn's predict_proba output, and consider the issue resolved when the provided assertions pass for the multi-class ensemble.

Written by the indexing model from the issue text.