[{'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 new_conv_layer(varName,\ninput,              # The previous layer.\nnum_input_channels, # Num. channels in prev. layer.\nfilter_size,        # Width and height of each filter.\nnum_filters,        # Number of filters.\nuse_pooling=True):  # Use 2x2 max-pooling.\nshape = [filter_size, filter_size, num_input_channels, num_filters]\nweights = new_weights(shape=shape)\nbiases = new_biases(length=num_filters)\nlayer = tf.nn.conv2d(input=input,\nfilter=weights,\nstrides=[1, 1, 1, 1],\npadding='SAME',name=varName)\nlayer += biases\nif use_pooling:\nlayer = tf.nn.max_pool(value=layer,\nksize=[1, 2, 2, 1],\nstrides=[1, 2, 2, 1],\npadding='SAME')\nlayer = tf.nn.relu(layer)\nreturn layer, weights"}, {'role': 'assistant', 'content': "['modelling']"}]