gpu_performance_state: 2
use_tpu: False
```
Again, inference time and required memory for inference are measured, but this time for customized configurations
of the BertModel class. This feature can especially be helpful when deciding for which configuration the model
should be trained.
Benchmark best practices
This section lists a couple of best practices one should be aware of when benchmarking a model.
Currently, only single device benchmarking is supported. When benchmarking on GPU, it is recommended that the user
  specifies on which device the code should be run by setting the CUDA_VISIBLE_DEVICES environment variable in the
  shell, e.g. export CUDA_VISIBLE_DEVICES=0 before running the code.
The option no_multi_processing should only be set to True for testing and debugging. To ensure accurate
