[{'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 train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):\n"""\nTrain the GAN\n:param epoch_count: Number of epochs\n:param batch_size: Batch Size\n:param z_dim: Z dimension\n:param learning_rate: Learning Rate\n:param beta1: The exponential decay rate for the 1st moment in the optimizer\n:param get_batches: Function to get batches\n:param data_shape: Shape of the data\n:param data_image_mode: The image mode to use for images ("RGB" or "L")\n"""\n_, image_width, image_height, num_channels = data_shape\nreal_input, z, learning_rate_t = model_inputs(image_width, image_height, num_channels, z_dim)\nd_loss_t, g_loss_t = model_loss(real_input, z, num_channels)\nd_train_opt, g_train_opt = model_opt(d_loss_t, g_loss_t, learning_rate, beta1)\nd_losses = []\ng_losses = []\nwith tf.Session() as sess:\nsess.run(tf.global_variables_initializer())\nstep = 0\nprint_every = 20\nshow_every = 100\nfor epoch_i in range(epoch_count):\nfor batch_images in get_batches(batch_size):\nbatch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))\nfeed = {real_input: batch_images, z: batch_z, learning_rate_t: learning_rate}\n_, _ = sess.run([g_loss_t, g_train_opt], feed_dict=feed)\ng_loss, _ = sess.run([g_loss_t, g_train_opt], feed_dict=feed)\nd_loss, _ = sess.run([d_loss_t, d_train_opt], feed_dict=feed)\nd_losses.append(d_loss)\ng_losses.append(g_loss)\nif step%print_every==0:\nprint("Step {}, (Epoch {}/{})...".format(step, epoch_i+1, epoch_count),\n"Discriminator Loss: {:.4f}...".format(d_loss),\n"Generator Loss: {:.4f}".format(g_loss))\nif step%show_every==0:\nshow_generator_output(sess, 64, z, num_channels, data_image_mode)\nstep += 1'}, {'role': 'assistant', 'content': "['modelling']"}]