save reconstructed data points from variational autoencoder as original MNIST

I have a VAE implementation that generates images from the latent distribution. I want to save those images as we have in the original dataset. For example, my VAE generates a data point, using following code:

data_point = decoder.predict(sample_2).reshape(28,28,1)

plt.figure(figsize=(4, 4))
plt.imshow(data_point, cmap = plt.cm.gray), plt.axis('off')


plt.show()

and I can see it as image (number 4 from MNIST).

If I look at the value of data_point, it's something like this:

array([[[4.03011961e-13], [2.21622661e-13], [1.77334818e-13], [7.62046296e-13], [2.77884297e-13], [2.07368519e-13], [8.03054997e-13], [2.32846815e-12], [3.30792956e-13], [5.10265875e-13], [4.53714377e-13], [7.72020902e-13], [2.40072452e-15], [5.33155790e-18], [1.82554410e-17], [1.94275460e-14], [7.08261032e-13], [1.93895017e-13], [1.41169140e-13], [2.54418963e-13], [1.84164587e-13], [5.63674216e-13], [2.41039881e-13], [1.09983593e-12], [3.10923162e-13], [6.10170389e-13], [2.82728566e-13], [8.62359446e-13]],

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​

How do I transform it back to 28,28,1 shape?