Instead of benchmarking pre-trained models via their model identifier, e.g. bert-base-uncased, the user can
alternatively benchmark an arbitrary configuration of any available model class. In this case, a list of
configurations must be inserted with the benchmark args as follows.
```py
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments, BertConfig
args = PyTorchBenchmarkArguments(
...     models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512]
... )
config_base = BertConfig()
config_384_hid = BertConfig(hidden_size=384)
config_6_lay = BertConfig(num_hidden_layers=6)
benchmark = PyTorchBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
benchmark.run()
====================       INFERENCE - SPEED - RESULT       ====================
