[{'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 = tf.Variable(tf.truncated_normal(([conv_ksize[0],\nconv_ksize[1],\nx_tensor.get_shape().as_list()[-1],\nconv_num_outputs]),\nmean=0.0,\nstddev=0.1,\ndtype=tf.float32,\nseed=None,\nname=None))\nb = tf.Variable(tf.zeros([conv_num_outputs]))\nx = tf.nn.conv2d(x_tensor, W,\nstrides=[1, *conv_strides, 1],\npadding=\'SAME\')\nx = tf.nn.bias_add(x, b)\nx = tf.nn.relu(x)\nx = tf.nn.max_pool(x,\nksize = [1, *pool_ksize, 1],\nstrides = [1, *pool_strides, 1],\npadding = \'SAME\')\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'}]