[{'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:def train(model, epochs, file_writer):\nwith tf.Session() as sess:\nsess.run(tf.global_variables_initializer())\nn_batches = int(train_x.shape[1]/num_steps)\niterations = n_batches * epochs\nfor e in range(epochs):\nnew_state = sess.run(model.initial_state)\nloss = 0\nfor b, (x, y) in enumerate(get_batch([train_x, train_y], num_steps), 1):\niteration = e*n_batches + b\nstart = time.time()\nfeed = {model.inputs: x,\nmodel.targets: y,\nmodel.keep_prob: 0.5,\nmodel.initial_state: new_state}\nsummary, batch_loss, new_state, _ = sess.run([model.merged, model.cost,\nmodel.final_state, model.optimizer],\nfeed_dict=feed)\nloss += batch_loss\nend = time.time()\nprint('Epoch {}/{} '.format(e+1, epochs),\n'Iteration {}/{}'.format(iteration, iterations),\n'Training loss: {:.4f}'.format(loss/b),\n'{:.4f} sec/batch'.format((end-start)))\nfile_writer.add_summary(summary, iteration)"}, {'role': 'assistant', 'content': 'modelling'}]