wait=1,
        warmup=1,
        active=2),
    # on_trace_ready argument specifies the handler for the traces
    on_trace_ready=trace_handler
) as p:
    for idx in range(8):
        model(inputs)
        # profiler will trace iterations 2 and 3, and then 6 and 7 (counting from zero)
        p.step()
```
More usage examples can be found on the profiler recipe page. 
The PyTorch Profiler Tensorboard plugin has new features for:
* Distributed Training summary view with communications overview for NCCL
* GPU Utilization and SM Efficiency in Trace view and GPU operators view
* Memory Profiling view
* Jump to source when launched from Microsoft VSCode
* Ability for load traces from cloud object storage systems 
(Beta) Inference Mode API