class_docstr: Initialize the DeepSpeed Transformer Layer.
  Arguments:
      layer_id: The layer index starting from 0, e.g. if model has 24 transformer layers,
          layer_id will be 0,1,2...23 when each layer object is instantiated
      config: An object of DeepSpeedInferenceConfig
      mp_group: Model parallelism group initialized on the modeling side.
      quantize_scales: This argument groups all the layers' scales used for quantization
      quantize_groups: Number of groups used for quantizing the model
      merge_count: Shows the number of model-parallel checkpoints merged before running inference.
          We use this argument to control the quantization scale for the model parameters if a bigger
          quantize-grouping than 1 is used.
      mlp_extra_grouping: This flag is used to show a 2x higher number of groups used for the MLP part
          of a Transformer layer. We use this feature for quantization to reduce the convergence impact
          for specific downstream tasks.
code: def reset_cache(cls):
      if inference_module is not None:
          inference_module.reset_cache()
  
file_path: DeepSpeed/deepspeed/model_implementations/transformers/ds_transformer.py
imports: import torch
  import torch.nn
  from deepspeed import comm
  from deepspeed.utils.logging import log_dist
  from deepspeed.ops.transformer.inference.ds_mlp import DeepSpeedMLP
  from deepspeed.ops.transformer.inference.ds_attention import DeepSpeedSelfAttention, BloomSelfAttention
  from deepspeed.accelerator import get_accelerator
  from deepspeed.ops.op_builder import InferenceBuilder
  import deepspeed
label_desc: Use image node_id 2 for calling the DeepSpeedTransformerInference obj's underlying member method code with example usage: obj.reset_cache(cls) without return types