{"task": {"agent_timeout": 4800, "task": "huggingface__datasets-7160", "verifier_timeout": 4800, "instruction": "I want to be able to process JSON lines files that contain structured data where some records might be missing certain fields, without encountering errors when converting these records into a structured format. Currently, when I try to load such files, the system fails with a type error because it expects every record to have all the fields defined in the structure, but some records are missing one or more fields.\n\nSpecifically, I need the system to handle cases where, for example, a record might have an \"age\" field but be missing a \"name\" field, or vice versa, without throwing an error. Instead, for any missing field in a record, the system should treat that field as having a null value and proceed with the conversion smoothly. This way, all records are consistently structured with all expected fields present, and missing fields are filled with nulls appropriately.\n\nI want this functionality to be integrated into the data conversion process so that when I specify a target structure with certain fields (like both \"age\" and \"name\"), the system automatically accommodates records that lack some of these fields by inserting null values where the data is missing. This should work seamlessly during the conversion of array data to the specified feature structure, ensuring that the output includes all fields as defined, with nulls in place of any absent values in individual records.\n\nAdditionally, I need this change to resolve the specific issue where the system previously raised a TypeError when it encountered a mismatch between the actual fields present in the data and the expected fields in the target structure. Now, I expect the system to handle such mismatches gracefully by considering only the fields that are present and supplementing the rest with nulls, thus avoiding any conversion errors and producing a complete and consistent dataset.\n", "memory": "4g", "runnable": false, "difficulty": "hard", "language": "", "cpus": 2, "instruction_truncated": false, "category": "feature", "compose": false, "has_solution": true, "oracle": null, "docker_image": "", "taskset": "featbench", "tags": ["feature", "featbench"]}, "runs": []}