The documentation lists that as a method of `tvm.auto_scheduler.ComputeDAG` we
can get a Python code representation of the schedule with
[`print_python_code_from_state()`](https://tvm.apache.org/docs/api/python/auto_scheduler.html?highlight=auto_scheduler#tvm.auto_scheduler.ComputeDAG.print_python_code_from_state).
Described as:
> Print transform steps in the history of a State as TVM’s python schedule code.
>
> This is used to print transformation steps for debugging. Use
> apply_steps_from_state if you want to get a schedule for code generation.
However, I am trying to figure out to use it. I have a simple model with a
single operation, and have loaded it into the two variables: `mod` and
`params`.
Additionally, I have performed auto-scheduling, and have a tuned log-file in
the `json` format.
>From this, how would I get this printed schedule?
If I try building a `tvm.auto_scheduler.ComputeDAG` object by passing `mod` to
it I get a `RecursionError: maximum recursion depth exceeded while calling a
Python object` error.
Same if I try and get the Ansor workload from it:
```python
tasks, task_weights = auto_scheduler.extract_tasks(
mod["main"], params, device_info['target_string'],
device_info['target_host']
)
for i, t in enumerate(tasks):
tgt_dag = str(t.compute_dag)
tvm.auto_scheduler.ComputeDAG(tgt_dag)
```
Even if they did work however, I'm not sure how I'd link it to my auto-schedule
`JSON` file.
The file `python/tvm/auto_scheduler/relay_integration.py` has a function
`auto_schedule_topi()` instantiates a `ComputeDAG`, though the docstring says:
> Note: This is used internally for relay integration. Do not use this as a
> general user-facing API.
Any pointers on getting this Python schedule for a tuned model?
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