{"task": {"agent_timeout": 1800, "task": "436", "verifier_timeout": 1800, "instruction": "# 436: DS-1000 Task\n\n## Prompt\nProblem:\nInput example:\nI have a numpy array, e.g.\na=np.array([[0,1], [2, 1], [4, 8]])\nDesired output:\nI would like to produce a mask array with the max value along a given axis, in my case axis 1, being True and all others being False. e.g. in this case\nmask = np.array([[False, True], [True, False], [False, True]])\nAttempt:\nI have tried approaches using np.amax but this returns the max values in a flattened list:\n>>> np.amax(a, axis=1)\narray([1, 2, 8])\nand np.argmax similarly returns the indices of the max values along that axis.\n>>> np.argmax(a, axis=1)\narray([1, 0, 1])\nI could iterate over this in some way but once these arrays become bigger I want the solution to remain something native in numpy.\nA:\n<code>\nimport numpy as np\na = np.array([[0, 1], [2, 1], [4, 8]])\n</code>\nmask = ... # 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": []}