{"task": {"agent_timeout": 1800, "task": "362", "verifier_timeout": 1800, "instruction": "# 362: DS-1000 Task\n\n## Prompt\nProblem:\n\n>>> arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n>>> del_col = [1, 2, 4, 5]\n>>> arr\narray([[ 1,  2,  3,  4],\n       [ 5,  6,  7,  8],\n       [ 9, 10, 11, 12]])\nI am deleting some columns(in this example, 1st, 2nd and 4th)\ndef_col = np.array([1, 2, 4, 5])\narray([[ 3],\n       [ 7],\n       [ 11]])\nNote that del_col might contain out-of-bound indices, so we should ignore them.\nAre there any good way ? Please consider this to be a novice question.\nA:\n<code>\nimport numpy as np\na = np.arange(12).reshape(3, 4)\ndel_col = np.array([1, 2, 4, 5])\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": []}