code: def __call__(self, x: Tensor) -> Tensor:
      assert x.shape[0] == 1, "only BS=1"
      g = self.gate(x).float().exp()
      choice = g.data().tolist()[0][0]
      top = sorted(enumerate(choice), key=lambda x: -x[1])
      norm = top[0][1] + top[1][1]
      e1, e2 = self.experts[top[0][0]], self.experts[top[1][0]]
      ret = e1(x.to(e1.w1.weight.device)).to(x.device) * Tensor(
          [top[0][1] / norm]
      ) + e2(x.to(e2.w1.weight.device)).to(x.device) * Tensor(
          [top[1][1] / norm]
      )
      return ret
  
file_path: tinygrad/examples/mixtral.py
imports: import functools, argparse, pathlib
  from tqdm import tqdm
  from tinygrad import Tensor, nn, Device, GlobalCounters, Variable
  from tinygrad.helpers import Timing, Profiling
  from tinygrad.nn.state import torch_load, get_state_dict
  from extra.models.llama import FeedForward, Transformer
label_desc: Use image node_id 2 for calling the MixtureFeedForward obj's underlying member method code with example usage: obj.__call__(x) and returns: ret