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plt = x_in.shape[1]
augmented_images = []
for i in range(x_in.shape[0]):
x = x_in[i, 0:plt, :]
y = x_in[i, plt:, :]
y_hat = inputs[i, plt:]
y_hat = y_hat[:, 0]
if not p.randint(0, 1) == 1:
y_hat = 1 – y_hat
y_hat[y_hat == 0] = 0
y = p.normal(y[:], y_hat[:], 1, lognorm_std[:], loc=mu_lognorm[i], scale=mu_lognorm[i], seed=1)
y = 1-y
#print (‘%d %d %d %d %d %d %d’ % (np.where(y_hat > 0.5)[0], np.where(y_hat > 0.5)[1], np.where(y_hat > 0.5)[0], np.where(y_hat > 0.5)[0]))
augmented_images.append(np.expand_dims(y[:], axis=0))
#print (np.where(y_hat > 0.5)[0], np.where(y_hat > 0.5)[1])
aug_images_train.append(np.