[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:with pm.Model() as model:\np1_empt_mu = pm.Flat('plate1 empty mean')\np1_empt_sd = pm.Exponential('plate1 empty stds', lam=1)\np1_empt_like = pm.Normal('plate1 empty', mu=p1_empt_mu, sd=p1_empt_sd, observed=plate1_empty)\np1_ctrl_mu = pm.Flat('plate1 control mean')\np1_ctrl_sd = pm.Exponential('plate1 control stds', lam=1)\np1_ctrl_like = pm.Normal('plate1 control', mu=p1_ctrl_mu, sd=p1_ctrl_sd, observed=plate1_control)\np1_smpl_mu = pm.Flat('plate1 sample mean', shape=n_genotypes)\np1_smpl_sd = pm.Exponential('plate1 sample stds', lam=1, shape=n_genotypes)\np1_smpl_like = pm.Normal('plate1 sample', mu=p1_smpl_mu, sd=p1_smpl_sd, observed=plate1_sample_data, shape=n_genotypes)\np1_ctrl_norm = pm.Deterministic('plate1 control normalized', p1_ctrl_mu - p1_empt_mu)\np1_samp_norm = pm.Deterministic('plate1 sample normalized', p1_smpl_mu - p1_empt_mu)\np1_fold = pm.Deterministic('plate1 fold', p1_samp_norm / p1_ctrl_norm)\np2_empt_mu = pm.Flat('plate2 empty mean')\np2_empt_sd = pm.Exponential('plate2 empty stds', lam=1)\np2_empt_like = pm.Normal('plate2 empty', mu=p2_empt_mu, sd=p2_empt_sd, observed=plate2_empty)\np2_ctrl_mu = pm.Flat('plate2 control mean')\np2_ctrl_sd = pm.Exponential('plate2 control stds', lam=1)\np2_smpl_mu = pm.Flat('plate2 sample mean', shape=n_genotypes)\np2_smpl_sd = pm.Exponential('plate2 sample stds', lam=1, shape=n_genotypes)\np2_ctrl_like = pm.Normal('plate2 control', mu=p2_ctrl_mu, sd=p2_ctrl_sd, observed=plate2_control)\np2_smpl_like = pm.Normal('plate2 sample', mu=p2_smpl_mu, sd=p2_smpl_sd, observed=plate2_sample_data, shape=n_genotypes)\np2_ctrl_norm = pm.Deterministic('plate2 control normalized', p2_ctrl_mu - p2_empt_mu)\np2_samp_norm = pm.Deterministic('plate2 sample normalized', p2_smpl_mu - p2_empt_mu)\np2_fold = pm.Deterministic('plate2 fold', p2_samp_norm / p2_ctrl_norm)\np3_empt_mu = pm.Flat('plate3 empty mean')\np3_empt_sd = pm.Exponential('plate3 empty stds', lam=1)\np3_empt_like = pm.Normal('plate3 empty', mu=p3_empt_mu, sd=p3_empt_sd, observed=plate3_empty)\np3_ctrl_mu = pm.Flat('plate3 control mean')\np3_ctrl_sd = pm.Exponential('plate3 control stds', lam=1)\np3_smpl_mu = pm.Flat('plate3 sample mean', shape=n_genotypes)\np3_smpl_sd = pm.Exponential('plate3 sample stds', lam=1, shape=n_genotypes)\np3_ctrl_like = pm.Normal('plate3 control', mu=p3_ctrl_mu, sd=p3_ctrl_sd, observed=plate3_control)\np3_smpl_like = pm.Normal('plate3 sample', mu=p3_smpl_mu, sd=p3_smpl_sd, observed=plate3_sample_data, shape=n_genotypes)\np3_ctrl_norm = pm.Deterministic('plate3 control normalized', p3_ctrl_mu - p3_empt_mu)\np3_samp_norm = pm.Deterministic('plate3 sample normalized', p3_smpl_mu - p3_empt_mu)\np3_fold = pm.Deterministic('plate3 fold', p3_samp_norm / p3_ctrl_norm)\naverage_fold = pm.Deterministic('average fold', (p1_fold + p2_fold + p3_fold) / 3)"}, {'role': 'assistant', 'content': 'modelling'}]