class_docstr: Computes linear operators within one GPU on different experts simutaneously.
code: def forward(
      ctx, global_input_buf, fwd_expert_count, weight, bias=None
  ):
      global_output_buf = fmoe_cuda.linear_forward(
          global_input_buf, fwd_expert_count, weight, bias
      )
      variables = (global_input_buf, fwd_expert_count, weight, bias)
      ctx.save_for_backward(*variables)
      return global_output_buf
  
imports: import torch
  from torch.autograd import Function
  import fmoe_cuda
  from .utils import get_torch_default_comm
  from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
  from copy import deepcopy
label_desc: Use image node_id 1 for calling the MOELinear obj's underlying member method code with example usage: obj.forward(ctx, global_input_buf, fwd_expert_count, weight, bias) and returns: global_output_buf