{"task": {"agent_timeout": 1800, "task": "308", "verifier_timeout": 1800, "instruction": "# 308: DS-1000 Task\n\n## Prompt\nProblem:\nI am waiting for another developer to finish a piece of code that will return an np array of shape (100,2000) with values of either -1,0, or 1.\nIn the meantime, I want to randomly create an array of the same characteristics so I can get a head start on my development and testing. The thing is that I want this randomly created array to be the same each time, so that I'm not testing against an array that keeps changing its value each time I re-run my process.\nI can create my array like this, but is there a way to create it so that it's the same each time. I can pickle the object and unpickle it, but wondering if there's another way.\nr = np.random.randint(3, size=(100, 2000)) - 1\nSpecifically, I want r_old, r_new to be generated in the same way as r, but their result should be the same.\nA:\n<code>\nimport numpy as np\n</code>\nr_old, r_new = ... # put solution in these variables\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": []}