{"task": {"agent_timeout": 1800, "task": "933", "verifier_timeout": 1800, "instruction": "# 933: DS-1000 Task\n\n## Prompt\nProblem:\n\nI have written a custom model where I have defined a custom optimizer. I would like to update the learning rate of the optimizer when loss on training set increases.\n\nI have also found this: https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate where I can write a scheduler, however, that is not what I want. I am looking for a way to change the value of the learning rate after any epoch if I want.\n\nTo be more clear, So let's say I have an optimizer:\n\noptim = torch.optim.SGD(..., lr=0.01)\nNow due to some tests which I perform during training, I realize my learning rate is too high so I want to change it to say 0.001. There doesn't seem to be a method optim.set_lr(0.001) but is there some way to do this?\n\n\nA:\n\n<code>\nimport numpy as np\nimport pandas as pd\nimport torch\noptim = load_data()\n</code>\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": []}