code: def forward(self, inputs, **kwargs):
      b, n, d, e = *inputs.shape, self.num_experts
      dispatch_tensor, combine_tensor, loss = self.gate(inputs)
      expert_inputs = torch.einsum(
          "bnd,bnec->ebcd", inputs, dispatch_tensor
      )
  
      # Now feed the expert inputs through the experts.
      orig_shape = expert_inputs.shape
      expert_inputs = expert_inputs.reshape(e, -1, d)
      expert_outputs = self.experts(expert_inputs)
      expert_outputs = expert_outputs.reshape(*orig_shape)
  
      output = torch.einsum(
          "ebcd,bnec->bnd", expert_outputs, combine_tensor
      )
      return output, loss * self.loss_coef
  
imports: import torch
  from torch import nn
  import torch.nn.functional
  import math
  from inspect import isfunction
label_desc: Use image node_id 2 for calling the MoE obj's underlying member method code with example usage: obj.forward(inputs) and returns: output,  unknown