class_docstr: A wrapper for the Slice function to support auto-differentiation.
class_docstr_tok: ["A","wrapper","for","the","Slice","function","to","support","auto-differentiation","."]
code: def backward(ctx, grad_out):
      world_size, group = ctx.args
      tensor_list = [
          torch.empty_like(grad_out) for _ in range(world_size)
      ]
      torch.distributed.all_gather(tensor_list, grad_out, group=group)
      torch.cuda.synchronize()
      grad_out = torch.cat(tensor_list, dim=0)
      return grad_out, None, None, None
  
imports: import torch
  from torch.autograd import Function
  import fmoe_cuda
  from .utils import get_torch_default_comm
label_desc: Use image node_id 2 for calling the Slice obj's underlying member method code with example usage: obj.backward(ctx, grad_out) and returns: grad_out,  None,  None,  None