[{'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:from sklearn.feature_extraction.image import grid_to_graph\nfor i in range(len(lena_patches)):\nlena_patches[i] = np.reshape(lena_patches[i], (-1, 1))\nX = np.concatenate(lena_patches, axis =1)\nprint(X.shape)\nconnectivity = grid_to_graph( lena.shape[0], lena.shape[1] )\nn_clusters = 10  # number of regions\nmodel = AgglomerativeClustering(linkage=\'ward\',\nconnectivity=connectivity,\nn_clusters=n_clusters)\nmodel.fit(X)\nlabel = np.reshape(model.labels_, lena.shape)\nprint("Number of pixels: ", label.size)\nprint("Number of clusters: ", np.unique(label).size)\nplt.figure(figsize=(15, 5))\ntry:\nvalues = model.cluster_centers_.squeeze()\nlena_compressed = np.choose(model.labels_, values)\nlena_compressed.shape = lena.shape\nplt.subplot(1,3,2)\nplt.imshow(lena_compressed, cmap=plt.cm.gray)\nexcept:\nplt.subplot(1,3,2)\nplt.imshow(label, cmap=plt.cm.gray)\nplt.subplot(1,3,1)\nplt.imshow(lena, cmap=plt.cm.gray)\nplt.subplot(1,3,3)\nplt.imshow(lena, cmap=plt.cm.gray)\nfor lab in range(n_clusters):\nplt.contour(label == lab, contours=1,\ncolors=[plt.cm.spectral(lab / float(n_clusters))])\nplt.show()'}, {'role': 'assistant', 'content': "['helper_functions', 'data_exploration', 'data_preprocessing', 'modelling']"}]