{"task": {"agent_timeout": 1800, "task": "307", "verifier_timeout": 1800, "instruction": "# 307: DS-1000 Task\n\n## Prompt\nProblem:\nFollowing-up from this question years ago, is there a \"shift\" function in numpy? Ideally it can be applied to 2-dimensional arrays, and the numbers of shift are different among rows.\nExample:\nIn [76]: xs\nOut[76]: array([[ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],\n\t\t [ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.]])\nIn [77]: shift(xs, [1,3])\nOut[77]: array([[nan,   0.,   1.,   2.,   3.,   4.,   5.,   6.,\t7.,\t8.], [nan, nan, nan, 1.,  2.,  3.,  4.,  5.,  6.,  7.])\nIn [78]: shift(xs, [-2,-3])\nOut[78]: array([[2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  nan,  nan], [4.,  5.,  6.,  7.,  8.,  9., 10., nan, nan, nan]])\nAny help would be appreciated.\nA:\n<code>\nimport numpy as np\na = np.array([[ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.],\n\t\t[1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.]])\nshift = [-2, 3]\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": []}