{"task": {"agent_timeout": 1800, "task": "507", "verifier_timeout": 1800, "instruction": "# 507: DS-1000 Task\n\n## Prompt\nProblem:\nI want to process a gray image in the form of np.array. \n*EDIT: chose a slightly more complex example to clarify\nSuppose\nim = np.array([ [0,0,0,0,0,0] [0,0,1,1,1,0] [0,1,1,0,1,0] [0,0,0,1,1,0] [0,0,0,0,0,0]])\nI'm trying to create this:\n[ [0,1,1,1], [1,1,0,1], [0,0,1,1] ]\nThat is, to remove the peripheral zeros(black pixels) that fill an entire row/column.\nI can brute force this with loops, but intuitively I feel like numpy has a better means of doing this.\nA:\n<code>\nimport numpy as np\nim = np.array([[0,0,0,0,0,0],\n               [0,0,1,1,1,0],\n               [0,1,1,0,1,0],\n               [0,0,0,1,1,0],\n               [0,0,0,0,0,0]])\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": []}