DeepSHAP beeswarm plot only shows blue dots and does not have a color gradient to show feature values

Question

I tried running an example on SHAP Deep Explainer from this link using this Titanic dataset. This is the code from the example:

# import package
import shap
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras import optimizers
import warnings 
warnings.filterwarnings('ignore')
%matplotlib inline
import os
# load data
os.chdir('/titanic/')
train_data = pd.read_csv('./train.csv', index_col=0)
test_data = pd.read_csv('./test.csv', index_col=0)
train_data.head()

def data_preprocessing(df):
    df = df.drop(columns=['Name', 'Ticket', 'Cabin'])
    
    # fill na
    df[['Age']] = df[['Age']].fillna(value=df[['Age']].mean())
    df[['Embarked']] = df[['Embarked']].fillna(value=df['Embarked'].value_counts().idxmax())
    df[['Fare']] = df[['Fare']].fillna(value=df[['Fare']].mean())
    
    # categorical features into numeric
    df['Sex'] = df['Sex'].map( {'female': 1, 'male': 0} ).astype(int)
    
    # one-hot encoding
    embarked_one_hot = pd.get_dummies(df['Embarked'], prefix='Embarked')
    df = df.drop('Embarked', axis=1)
    df = df.join(embarked_one_hot)
    
    return df

# train data processing
train_data = data_preprocessing(train_data)
train_data.isnull().sum()
# create data for training
x_train = train_data.drop(['Survived'], axis=1).values
# Check test data
test_data.isnull().sum()
# scale
scale = StandardScaler()
x_train = scale.fit_transform(x_train)
# prepare y_train
y_train = train_data['Survived'].values
test_data = data_preprocessing(test_data)
x_test = test_data.values.astype(float)
# scaling
x_test = scale.transform(x_test)
# Check test data
test_data.isnull().sum()

# build mlp
model = Sequential()
model.add(Dense(32, input_dim=x_train.shape[1], activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(8, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(2, activation='softmax'))
# compile model
model.compile(loss='sparse_categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
# fit model
model.fit(x_train, y_train, epochs=10, batch_size=64)

# compute SHAP values
explainer = shap.DeepExplainer(model, x_train)
shap_values = explainer.shap_values(x_test)

shap.summary_plot(shap_values[0], plot_type = 'bar', feature_names = test_data.columns)

shap.initjs()
shap.force_plot(explainer.expected_value[0].numpy(), shap_values[0][0], features = test_data.columns)

shap.decision_plot(explainer.expected_value[0].numpy(), shap_values[0][0], features = test_data.iloc[0,:], feature_names = test_data.columns.tolist())

shap.plots._waterfall.waterfall_legacy(explainer.expected_value[0].numpy(), shap_values[0][0], feature_names = test_data.columns)

There is no code for generating a beeswarm plot in the example, but I used

shap.summary_plot(shap_values[0], feature_names = test_data.columns)

and got a beeswarm plot. From my understanding, the color of the dots displays the original value of each feature, falling along a gradient of blue to red. However, the plot I got only has blue dots and doesn't have a gradient ruler on the side.

Here is the plot I got:

And here is what I expected (photo from https://shap.readthedocs.io/en/latest/example_notebooks/api_examples/plots/beeswarm.html):

Any suggestions on what could have caused this and what I can do to get the colors would be greatly appreciated. Thank you!