{"task": {"agent_timeout": 1800, "task": "939", "verifier_timeout": 1800, "instruction": "# 939: DS-1000 Task\n\n## Prompt\nProblem:\n\nI'm trying to convert a torch tensor to pandas DataFrame.\nHowever, the numbers in the data is still tensors, what I actually want is numerical values.\nThis is my code\nimport torch\nimport pandas as  pd\nx = torch.rand(4,4)\npx = pd.DataFrame(x)\nAnd px looks like\n\n0   1   2   3\ntensor(0.3880)  tensor(0.4598)  tensor(0.4239)  tensor(0.7376)\ntensor(0.4174)  tensor(0.9581)  tensor(0.0987)  tensor(0.6359)\ntensor(0.6199)  tensor(0.8235)  tensor(0.9947)  tensor(0.9679)\ntensor(0.7164)  tensor(0.9270)  tensor(0.7853)  tensor(0.6921)\nHow can I just get rid of 'tensor'?\n\n\nA:\n\n<code>\nimport numpy as np\nimport torch\nimport pandas as pd\nx = load_data()\n</code>\npx = ... # 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": []}