[{'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 generator(z, out_channel_dim, is_train=True):\n"""\nCreate the generator network\n:param z: Input z\n:param out_channel_dim: The number of channels in the output image\n:param is_train: Boolean if generator is being used for training\n:return: The tensor output of the generator\n"""\nwith tf.variable_scope(\'generator\', reuse= not is_train):\nweights = tf.contrib.layers.xavier_initializer_conv2d()\nfc = tf.layers.dense(z, 3*3*256)\nfc = tf.reshape(fc, (-1, 3, 3, 256))\nfc = tf.contrib.layers.batch_norm(fc, epsilon=1e-5)\nfc = lrelu(fc)\nconv1 = tf.layers.conv2d_transpose(fc, 128, 3,\nstrides= 2,\npadding=\'valid\',\nkernel_initializer = weights)\nconv1 = tf.contrib.layers.batch_norm(conv1, epsilon=1e-5)\nconv1 = lrelu(conv1)\nconv2 = tf.layers.conv2d_transpose(conv1, 64, 3,\nstrides= 2,\npadding=\'same\',\nkernel_initializer = weights)\nconv2 = tf.contrib.layers.batch_norm(conv2, epsilon=1e-5)\nconv2 = lrelu(conv2)\nconv3 = tf.layers.conv2d_transpose(conv2, out_channel_dim, 3,\nstrides= 2,\npadding=\'same\',\nkernel_initializer = weights)\nreturn tf.tanh(conv3)\n"""\nDON\'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n"""\ntests.test_generator(generator, tf)'}, {'role': 'assistant', 'content': "['modelling']"}]