[{'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 model_opt(d_loss, g_loss, learning_rate, beta1):\n"""\nGet optimization operations\n:param d_loss: Discriminator loss Tensor\n:param g_loss: Generator loss Tensor\n:param learning_rate: Learning Rate Placeholder\n:param beta1: The exponential decay rate for the 1st moment in the optimizer\n:return: A tuple of (discriminator training operation, generator training operation)\n"""\nt_vars = tf.trainable_variables()\nd_vars = [var for var in t_vars if var.name.startswith(\'discriminator\')]\ng_vars = [var for var in t_vars if var.name.startswith(\'generator\')]\nwith tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\nd_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)\ng_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)\nreturn d_train_opt, g_train_opt\n"""\nDON\'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n"""\ntests.test_model_opt(model_opt, tf)'}, {'role': 'assistant', 'content': "['modelling']"}]