[{'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 slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None],\nxy_window=(64, 64), xy_overlap=(0.5, 0.5)):\n"""\nFunction that takes an image, start and stop positions in both x and y,\nwindow size (x and y dimensions),\nand overlap fraction (for both x and y),\nand creates a list of windows to be searched on, for a given image\n"""\nif x_start_stop[0] == None:\nx_start_stop[0] = 0\nif x_start_stop[1] == None:\nx_start_stop[1] = img.shape[1]\nif y_start_stop[0] == None:\ny_start_stop[0] = 0\nif y_start_stop[1] == None:\ny_start_stop[1] = img.shape[0]\nxspan = x_start_stop[1] - x_start_stop[0]\nyspan = y_start_stop[1] - y_start_stop[0]\nnx_pix_per_step = np.int(xy_window[0] * (1 - xy_overlap[0]))\nny_pix_per_step = np.int(xy_window[1] * (1 - xy_overlap[1]))\nnx_windows = np.int(xspan / nx_pix_per_step) - 1\nny_windows = np.int(yspan / ny_pix_per_step) - 1\nwindow_list = []\nfor ys in range(ny_windows):\nfor xs in range(nx_windows):\nstartx = xs * nx_pix_per_step + x_start_stop[0]\nendx = startx + xy_window[0]\nstarty = ys * ny_pix_per_step + y_start_stop[0]\nendy = starty + xy_window[1]\nwindow_list.append(((startx, starty), (endx, endy)))\nreturn window_list'}, {'role': 'assistant', 'content': 'data_preprocessing'}]