class_docstr: Initialize the DeepSpeed MoE 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.
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code: def _alltoall(self, dispatched_attention):
      if dist.get_world_size(group=self.ep_group) > 1:
          dispatched_input = torch.empty_like(dispatched_attention)
          dist.all_to_all_single(
              dispatched_input,
              dispatched_attention,
              group=self.ep_group,
          )
          return dispatched_input
      else:
          return dispatched_attention
  
imports: import json
  import math
  import torch
  from torch.autograd import Function
  import torch.nn
  from .ds_attention import DeepSpeedSelfAttention
  from .config import DeepSpeedInferenceConfig
  from ....moe.sharded_moe import TopKGate
  from deepspeed import comm
  from deepspeed.accelerator import get_accelerator
  from deepspeed.ops.op_builder import InferenceBuilder
label_desc: Use image node_id 5 for calling the DeepSpeedMoEInference obj's underlying member method code with example usage: obj._alltoall(dispatched_attention) and returns: dispatched_input, dispatched_attention