[Python] xgboost : TypeError: predict() got an unexpected keyword argument 'pred_contribs'

I trained a model using xgboost v0.90 to be compatible with AWS SageMaker ML Engine. I am doing the usual encoding and hyper-parameter tuning. Some code below:

import pandas as pd
import pickle
from xgboost import XGBRegressor
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder


# split df into train and test
X_train, X_test, y_train, y_test = train_test_split(df.iloc[:,0:21], df.iloc[:,-1], test_size=0.1)

X_train.shape
(1000,21)

# Encode categorical variables
cat_vars = ['cat1','cat2','cat3']
cat_transform = ColumnTransformer([('cat', OneHotEncoder(handle_unknown='ignore'), cat_vars)], remainder='passthrough')

encoder = cat_transform.fit(X_train)
X_train = encoder.transform(X_train)
X_test = encoder.transform(X_test)

X_train.shape
(1000,420)

# Define a xgboost regression model
model = XGBRegressor()

# Do hyper-parameter tuning
.....

# Fit model
model.fit(X_train, y_train)


# Forecast on test data
y_pred = model.predict(X_test, pred_contribs=True)
y_pred


I have installed SHAP and based on the documentation [1], .predict() takes pred_contribs argument. Traceback:

TypeError Traceback (most recent call last)
<ipython-input-1119-37e607e853fd> in <module>
1 # Forecast on test data
----> 2 y_pred = model.predict(X_test, pred_contribs=True)
3 y_pred

TypeError: predict() got an unexpected keyword argument 'pred_contribs'


[1]https://shap.readthedocs.io/en/late...ion of Tree SHAP.html?highlight=pred_contribs

[2]https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.Booster.predict

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