code: def __init__(
      self,
      num_experts: int,
      dim: int,
      hidden_dim: int,
      linear=nn.Linear,
  ):
      self.gate = nn.Linear(dim, num_experts, bias=False)
      self.experts = [
          FeedForward(dim, hidden_dim, linear)
          for _ in range(num_experts)
      ]
  
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 1 to create a new MixtureFeedForward object  with example: obj = MixtureFeedForward(num_experts, dim, hidden_dim, linear)