code: def __init__(
      self,
      layer_idx: int,
      num_layers: int,
      in_dim: int,
      latent_dim: int,
      dropout: float,
  ):
      super().__init__(in_dim, latent_dim, dropout)
      self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
  
      self.key = nn.Linear(in_dim, latent_dim, bias=False)
      self.value = nn.Linear(latent_dim, in_dim, bias=False)
      self.receptance = nn.Linear(in_dim, in_dim, bias=False)
  
      with torch.no_grad():
          for k, v in self.state_dict().items():
              for zero_k in ["value", "receptance"]:
                  if zero_k in k:
                      nn.init.zeros_(v)
                      break
              else:
                  nn.init.orthogonal_(v)
  
          _1_to_almost0 = 1.0 - (layer_idx / num_layers)
          x = torch.ones(1, 1, in_dim)
          for i in range(in_dim):
              x[0, 0, i] = i / in_dim
          self.time_mix_k = nn.Parameter(torch.pow(x, _1_to_almost0))
          self.time_mix_r = nn.Parameter(torch.pow(x, _1_to_almost0))
  
imports: import torch
  from torch import nn
  from torch import Tensor
  from .schema import ChannelMixerBase
  from ..convs import DepthWiseConv2d
  from ..common import Lambda
  from ..hijacks import HijackCustomLinear
  from ..activations import GEGLU
  from ..activations import Activation
label_desc: Use image node_id 1 to create a new RWKVChannelMixer object from inherited base classes: ChannelMixerBase with example: obj = RWKVChannelMixer(layer_idx, num_layers, in_dim, latent_dim, dropout)