[{'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:def get_color_pal(max_jobs_per_engines):\ncolor_pal = sns.cubehelix_palette(\nn_colors=max_jobs_per_engines,\nstart=2.3, rot=0.8, gamma=0.6, hue=0.8,\nlight=0.9, dark=0.3,\nreverse=True\n)\nreturn color_pal\ndef plot_meta_df(meta_df, figsize=None):\nmax_jobs_per_engines = meta_df['order'].max() + 1\nnum_engines = len(np.unique(meta_df['engine_id']))\ncolor_pal = get_color_pal(max_jobs_per_engines)\nif figsize is not None:\nfig = plt.figure(figsize=figsize)\nelse:\nfig = plt.figure(figsize=(max_jobs_per_engines * 6 * 2 / 12, 6))\nax = plt.axes()\nbar_height = 0.8\nfor ix, row in meta_df.iterrows():\nplt.bar(\nbottom=int(row.engine_id) , left=row.rel_started,\nwidth=row.rel_completed - row.rel_started, height=bar_height,\ncolor=color_pal[int(row['order'])], axes=ax\n)\nplt.xlabel('Time (second)')\nplt.ylabel('Engine')\nplt.yticks(\nnp.arange(num_engines) + bar_height / 2,\n['%2d' % ix for ix in np.arange(num_engines)]\n)\nreturn fig"}, {'role': 'assistant', 'content': "['result_visualization']"}]