[{'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(stddev=.1,\nshape=[conv_ksize[0],conv_ksize[1],x_tensor.get_shape().as_list()[3],conv_num_outputs]))\nb = tf.Variable(tf.zeros(conv_num_outputs))\nx_tensor = tf.nn.conv2d(\ninput=x_tensor,filter=w,\nstrides=[1,conv_strides[0],conv_strides[1],1],\npadding=\'SAME\')\nx_tensor = tf.nn.bias_add(x_tensor,b)\nx_tensor = tf.nn.relu(x_tensor)\nx_tensor = tf.nn.max_pool(\nvalue=x_tensor,\nksize=[1,pool_ksize[0],pool_ksize[1],1],\nstrides=[1,pool_strides[0],pool_strides[1],1],\npadding=\'SAME\')\nreturn x_tensor\n"""\nDON\'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n"""\ntests.test_con_pool(conv2d_maxpool)'}, {'role': 'assistant', 'content': 'modelling'}]