[{'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:"""\nDON\'T MODIFY ANYTHING IN THIS CELL\n"""\nsave_path = \'checkpoints/dev\'\n(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()\nmax_target_sentence_length = max([len(sentence) for sentence in source_int_text])\ntrain_graph = tf.Graph()\nwith train_graph.as_default():\ninput_data, targets, lr, keep_prob = model_inputs()\nsequence_length = tf.placeholder_with_default(max_target_sentence_length, None, name=\'sequence_length\')\ninput_shape = tf.shape(input_data)\ntrain_logits, inference_logits = seq2seq_model(\ntf.reverse(input_data, [-1]), targets, keep_prob, batch_size, sequence_length, len(source_vocab_to_int), len(target_vocab_to_int),\nencoding_embedding_size, decoding_embedding_size, rnn_size, num_layers, target_vocab_to_int)\ntf.identity(inference_logits, \'logits\')\nwith tf.name_scope("optimization"):\ncost = tf.contrib.seq2seq.sequence_loss(\ntrain_logits,\ntargets,\ntf.ones([input_shape[0], sequence_length]))\noptimizer = tf.train.AdamOptimizer(lr)\ngradients = optimizer.compute_gradients(cost)\ncapped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]\ntrain_op = optimizer.apply_gradients(capped_gradients)'}, {'role': 'assistant', 'content': 'modelling'}]