{"task": {"agent_timeout": 1800, "task": "968", "verifier_timeout": 1800, "instruction": "# 968: DS-1000 Task\n\n## Prompt\nProblem:\n\nI have the following torch tensor:\n\ntensor([[-0.2,  0.3],\n    [-0.5,  0.1],\n    [-0.4,  0.2]])\nand the following numpy array: (I can convert it to something else if necessary)\n\n[1 0 1]\nI want to get the following tensor:\n\ntensor([0.3, -0.5, 0.2])\ni.e. I want the numpy array to index each sub-element of my tensor. Preferably without using a loop.\n\nThanks in advance\n\n\nA:\n\n<code>\nimport numpy as np\nimport pandas as pd\nimport torch\nt, idx = load_data()\nassert type(t) == torch.Tensor\nassert type(idx) == np.ndarray\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": []}