[{'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 get_hog_features(img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=True):\nif vis == True:\nfeatures, hog_image = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),\ncells_per_block=(cell_per_block, cell_per_block), visualise=True, feature_vector=False)\nreturn features, hog_image\nelse:\nfeatures = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),\ncells_per_block=(cell_per_block, cell_per_block), visualise=False, feature_vector=feature_vec)\nreturn features\ngray = cv2.cvtColor(car_image, cv2.COLOR_RGB2GRAY)\nfeatures, hog_image = get_hog_features(gray, orient, pix_per_cell, cell_per_block, vis=True, feature_vec=False)\nfig = plt.figure()\nplt.subplot(121)\nplt.imshow(car_image, cmap='gray')\nplt.title('Example Car Image')\nplt.subplot(122)\nplt.imshow(hog_image, cmap='gray')\nplt.title('HOG Visualization')"}, {'role': 'assistant', 'content': 'data_exploration'}]