code: def __init__(
      self,
      dim,
      num_gates,
      eps=1e-9,
      outer_expert_dims=tuple(),
      second_policy_train="random",
      second_policy_eval="random",
      second_threshold_train=0.2,
      second_threshold_eval=0.2,
      capacity_factor_train=1.25,
      capacity_factor_eval=2.0,
  ):
      super().__init__()
  
      self.eps = eps
      self.num_gates = num_gates
      self.w_gating = nn.Parameter(
          torch.randn(*outer_expert_dims, dim, num_gates)
      )
  
      self.second_policy_train = second_policy_train
      self.second_policy_eval = second_policy_eval
      self.second_threshold_train = second_threshold_train
      self.second_threshold_eval = second_threshold_eval
      self.capacity_factor_train = capacity_factor_train
      self.capacity_factor_eval = capacity_factor_eval
  
imports: import torch
  from torch import nn
  import torch.nn.functional
  import math
  from inspect import isfunction
label_desc: Use image node_id 1 to create a new Top2Gating object from inherited base classes: nn with example: obj = Top2Gating(dim, num_gates, eps, outer_expert_dims, second_policy_train, second_policy_eval, second_threshold_train, second_threshold_eval, capacity_factor_train, capacity_factor_eval)