{"task": {"agent_timeout": 1800, "task": "670", "verifier_timeout": 1800, "instruction": "# 670: DS-1000 Task\n\n## Prompt\nProblem:\nI'm using tensorflow 2.10.0.\nI am building a custom metric to measure the accuracy of one class in my multi-class dataset during training. I am having trouble selecting the class. \nThe targets are reversed one hot (e.g: the class 0 label is [0 0 0 0 1]):\nI have 10 classes in total, so I need a n*10 tensor as result.\nNow I have a list of integer (e.g. [0, 6, 5, 4, 2]), how to get a tensor like(dtype should be int32):\n[[0 0 0 0 0 0 0 0 0 1]\n [0 0 0 1 0 0 0 0 0 0]\n [0 0 0 0 1 0 0 0 0 0]\n [0 0 0 0 0 1 0 0 0 0]\n [0 0 0 0 0 0 0 1 0 0]]\n\nA:\n<code>\nimport tensorflow as tf\n\nlabels = [0, 6, 5, 4, 2]\n</code>\nresult = ... # put solution in this variable\nBEGIN SOLUTION\n<code>\n\n## What to do\n- Edit `solution/solution.py` so the code passes the DS-1000 tests.\n- Do not access the internet or install new packages; required libraries are preinstalled in the Docker image.\n- Run tests locally via `bash tests/test.sh`.\n\n## Notes\n- Keep the variable names/signatures implied by the prompt/code_context.\n- The evaluator uses the original DS-1000 `code_context` (`test_execution` / `test_string`).\n", "memory": "", "runnable": false, "difficulty": "", "language": "", "cpus": "", "instruction_truncated": false, "category": "", "compose": false, "has_solution": true, "oracle": null, "docker_image": "ds1000:latest", "taskset": "ds1000", "tags": []}, "runs": []}