[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': "Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\nin_imgs = mnist.test.images[:10]\nnoisy_imgs = in_imgs + noise_factor * np.random.randn(*in_imgs.shape)\nnoisy_imgs = np.clip(noisy_imgs, 0., 1.)\nreconstructed = sess.run(decoded, feed_dict={inputs_: noisy_imgs.reshape((10, 28, 28, 1))})\nfor images, row in zip([noisy_imgs, reconstructed], axes):\nfor img, ax in zip(images, row):\nax.imshow(img.reshape((28, 28)), cmap='Greys_r')\nax.get_xaxis().set_visible(False)\nax.get_yaxis().set_visible(False)\nfig.tight_layout(pad=0.1)"}, {'role': 'assistant', 'content': "['data_exploration', 'result_visualization']"}]