{"task": {"agent_timeout": 1800, "task": "996", "verifier_timeout": 1800, "instruction": "# 996: DS-1000 Task\n\n## Prompt\nProblem:\n\nI have a tensor t, for example\n\n1 2\n3 4\n5 6\n7 8\nAnd I would like to make it\n\n0 0 0 0\n0 1 2 0\n0 3 4 0\n0 5 6 0\n0 7 8 0\n0 0 0 0\nI tried stacking with new=torch.tensor([0. 0. 0. 0.]) tensor four times but that did not work.\n\nt = torch.arange(8).reshape(1,4,2).float()\nprint(t)\nnew=torch.tensor([[0., 0., 0.,0.]])\nprint(new)\nr = torch.stack([t,new])  # invalid argument 0: Tensors must have same number of dimensions: got 4 and 3\nnew=torch.tensor([[[0., 0., 0.,0.]]])\nprint(new)\nr = torch.stack([t,new])  # invalid argument 0: Sizes of tensors must match except in dimension 0.\nI also tried cat, that did not work either.\n\n\nA:\n\n<code>\nimport numpy as np\nimport pandas as pd\nimport torch\nt = load_data()\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": []}