class_docstr: Initialize the DeepSpeed Transformer Config.
  Arguments:
      hidden_size: The hidden size of the transformer layer
      intermediate_size: The intermediate size of the feed-forward part of transformer layer
      heads: The number of heads in the self-attention of the transformer layer
      num_hidden_layers: The number of transformer layers
      layer_norm_eps: The epsilon value for the layer norm
      local_rank: Optional: The rank of GPU running the transformer kernel, it is not required
          to use if the model already set the current device, otherwise need to set it
          so that the transformer kernel can work on the right device
      mp_size (optional): This argument is mainly used to create the parameters on the kernel side
          using model-parallel architecture. If the client model already takes care of this, there is no
          need to pass this argument.
      fp16: Enable half-precision computation
      pre_layer_norm: Select between Pre-LN or Post-LN transformer architecture
      stochastic_mode:  Enable for high performance, please note that this flag has some level of
          non-determinism and can produce different results on different runs.  However, we have seen
          that by enabling it, the pretraining tasks such as BERT are not affected and can obtain
          a high accuracy level. On the other hand, for the downstream tasks, such as fine-tuning, we recommend
          to turn it off in order to be able to reproduce the same result through the regular kernel execution.
  
      scale_attention: If true, both q and k are scaled by 1/sqrt(attention_heads) before attention computation.
      return_tuple: if True, returns the transformer output as a tuple, otherwise returns as a tensor
      bigscience_bloom: This flag is added temporarily for supporting the BLOOM-176B model architecture.
code: def from_json_file(cls, json_file):
      with open(json_file, "r", encoding="utf-8") as reader:
          text = reader.read()
      return cls.from_dict(json.loads(text))
  
file_path: H2O/h2o_flexgen/benchmark/third_party/DeepSpeed/deepspeed/ops/transformer/inference/config.py
imports: import json
  from deepspeed.utils.types import ActivationFuncType
label_desc: Use image node_id 3 for calling the DeepSpeedInferenceConfig obj's underlying member method code with example usage: obj.from_json_file(cls, json_file) and returns: cls