class_docstr: Initialize an MoE layer.
  
  Arguments:
      hidden_size (int): the hidden dimension of the model, importantly this is also the input and output dimension.
      expert (torch.nn.Module): the torch module that defines the expert (e.g., MLP, torch.linear).
      num_experts (int, optional): default=1, the total number of experts per layer.
      ep_size (int, optional): default=1, number of ranks in the expert parallel world or group.
      k (int, optional): default=1, top-k gating value, only supports k=1 or k=2.
      capacity_factor (float, optional): default=1.0, the capacity of the expert at training time.
      eval_capacity_factor (float, optional): default=1.0, the capacity of the expert at eval time.
      min_capacity (int, optional): default=4, the minimum capacity per expert regardless of the capacity_factor.
      use_residual (bool, optional): default=False, make this MoE layer a Residual MoE (https://arxiv.org/abs/2201.05596) layer.
      noisy_gate_policy (str, optional): default=None, noisy gate policy, valid options are 'Jitter', 'RSample' or 'None'.
      drop_tokens (bool, optional): default=True, whether to drop tokens - (setting to False is equivalent to infinite capacity).
      use_rts (bool, optional): default=True, whether to use Random Token Selection.
      use_tutel (bool, optional): default=False, whether to use Tutel optimizations (if installed).
      enable_expert_tensor_parallelism (bool, optional): default=False, whether to use tensor parallelism for experts
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docstr: MoE forward
  
  Arguments:
      hidden_states (Tensor): input to the layer
      used_token (Tensor, optional): default: None, mask only used tokens
  
  Returns:
      A tuple including output, gate loss, and expert count.
  
      * output (Tensor): output of the model
  
      * l_aux (Tensor): gate loss value
  
      * exp_counts (int): expert count
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code: def forward(self, hidden_states, used_token=None):
      """MoE forward
  
      Arguments:
          hidden_states (Tensor): input to the layer
          used_token (Tensor, optional): default: None, mask only used tokens
  
      Returns:
          A tuple including output, gate loss, and expert count.
  
          * output (Tensor): output of the model
  
          * l_aux (Tensor): gate loss value
  
          * exp_counts (int): expert count
      """
      output = self.deepspeed_moe(hidden_states, used_token)
      if self.use_residual:
          # Residual MoE
          output_mlp = self.mlp(hidden_states)
          if type(output_mlp) is tuple:
              output_mlp = output_mlp[0]  # Ignore the bias term for now
          coef = self.coefficient(hidden_states)
          coef = torch.nn.functional.softmax(coef, dim=-1)
          output = output * coef[..., 0:1] + output_mlp * coef[..., 1:]
      return (
          output,
          self.deepspeed_moe.l_aux,
          self.deepspeed_moe.exp_counts,
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/DeepSpeed/deepspeed/moe/layer.py
imports: import torch
  from deepspeed.utils import log_dist
  from deepspeed.utils import groups
  from .sharded_moe import MOELayer, TopKGate
  from .experts import Experts
  import typing
label_desc: Use image node_id 4 for calling the MoE obj's underlying member method code with example usage: obj.forward(hidden_states, used_token) and returns: output,  self,  self