code: def __init__(self, args):
      super().__init__()
      sigma = args.init_method_std
  
      from fairseq.modules import BaseLayer
      from fmoe.layers import mark_module_parallel_comm
  
      args.decoder_embed_dim = args.hidden_size
      args.decoder_ffn_embed_dim = args.hidden_hidden_size
      args.base_shuffle = True
      args.base_sublayers = 1
      self.top_k = args.top_k
      self.models = nn.ModuleList()
      for i in range(args.top_k):
          model = BaseLayer(args)
          model.expert_centroids.dp_comm = "dp"
          mark_module_parallel_comm(model, "moe")
  
          rng = np.random.default_rng(
              np.random.randint(2048) + args.rank * 100000 + i
          )
          _megatron_init_method(model.expert_network[0].ff1, rng, sigma)
          std = sigma / math.sqrt(2.0 * args.num_layers)
          _megatron_init_method(model.expert_network[0].ff2, rng, std)
          self.models.append(model)
  
imports: import math
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from fmoe.transformer import FMoETransformerMLP
  from fmoe.utils import get_torch_default_comm
  from .balance import reset_gate_hook
  from .balance import generate_megatron_gate_hook
  from .distributed import set_moe_group
label_desc: Use image node_id 1 to create a new BaseMLP object from inherited base classes: nn with example: obj = BaseMLP(args)