[{'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 conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):\n"""\nApply convolution then max pooling to x_tensor\n:param x_tensor: TensorFlow Tensor\n:param conv_num_outputs: Number of outputs for the convolutional layer\n:param conv_ksize: kernal size 2-D Tuple for the convolutional layer\n:param conv_strides: Stride 2-D Tuple for convolution\n:param pool_ksize: kernal size 2-D Tuple for pool\n:param pool_strides: Stride 2-D Tuple for pool\n: return: A tensor that represents convolution and max pooling of x_tensor\n"""\nw_shape = [*conv_ksize, x_tensor.shape[3].value, conv_num_outputs]\nW = tf.Variable(tf.random_normal(w_shape, stddev=0.1))\nb = tf.Variable(tf.zeros(conv_num_outputs))\npadding = \'SAME\'\ncnn_strides = [1, *conv_strides, 1]\nx = tf.nn.conv2d(x_tensor, W, strides=cnn_strides, padding=padding)\nx = tf.nn.bias_add(x, b)\nx = tf.nn.relu(x)\npool_ksize = [1, *pool_ksize, 1]\npool_strides = [1, *pool_strides, 1]\nx = tf.nn.max_pool(x, pool_ksize, pool_strides, padding)\nreturn x\n"""\nDON\'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n"""\ntests.test_con_pool(conv2d_maxpool)'}, {'role': 'assistant', 'content': 'modelling'}]