Data augmentation Keras

from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
import matplotlib.pyplot as plt
# Visualizations will be shown in the notebook.
%matplotlib inline
import random
import numpy as np

datagen = ImageDataGenerator(
        rotation_range=40,
        width_shift_range=0.2,
        height_shift_range=0.2,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=False,
        dim_ordering='tf',
        fill_mode='nearest')


# reshape to be [samples][pixels][width][height]
print(X_test.shape)
# convert from int to float
X_test_ = X_test.astype('float32')

image = X_test[0].squeeze()
plt.figure(figsize=(4,4))
plt.imshow(image)
plt.figure(figsize=(9,9))


# fit parameters from data
datagen.fit(X_test_)
# configure batch size and retrieve one batch of images
for X_batch in datagen.flow(X_test_, batch_size=9):
    # create a grid of 3x3 images
    for i in range(0, 9):
        plt.subplot(330 + 1 + i)
        plt.imshow(X_batch[i])
    # show the plot
    plt.show()
    break

I have a question. How can determine how many images will be create ?