class_docstr: Lunar Multi-headed attention.
  See "Linformer: Self-Attention with Linear Complexity" for more details.
code: def reset_parameters(self):
      # Empirically observed the convergence to be much better with
      # the scaled initialization
      gain = 1.0 / math.sqrt(2.0)
      nn.init.xavier_uniform_(self.pq_proj.weight, gain=gain)
      nn.init.xavier_uniform_(self.q_proj.weight, gain=gain)
      if self.pq_proj.bias is not None:
          nn.init.constant_(self.pq_proj.bias, 0.0)
          nn.init.constant_(self.q_proj.bias, 0.0)
  
      if self.pc_proj is not None:
          nn.init.xavier_uniform_(self.pc_proj.weight, gain=gain)
          nn.init.xavier_uniform_(self.c_proj.weight, gain=gain)
          if self.pc_proj.bias is not None:
              nn.init.constant_(self.pc_proj.bias, 0.0)
              nn.init.constant_(self.c_proj.bias, 0.0)
      else:
          nn.init.xavier_uniform_(self.pk_proj.weight, gain=gain)
          nn.init.xavier_uniform_(self.pv_proj.weight, gain=gain)
          nn.init.xavier_uniform_(self.k_proj.weight, gain=gain)
          nn.init.xavier_uniform_(self.v_proj.weight, gain=gain)
          if self.pk_proj.bias is not None:
              nn.init.constant_(self.pk_proj.bias, 0.0)
              nn.init.constant_(self.pv_proj.bias, 0.0)
              nn.init.constant_(self.k_proj.bias, 0.0)
              nn.init.constant_(self.v_proj.bias, 0.0)
  
      nn.init.xavier_uniform_(self.out_proj.weight)
      if self.out_proj.bias is not None:
          nn.init.constant_(self.out_proj.bias, 0.0)
  
imports: import math
  from typing import Dict, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from fairseq import utils
  from fairseq.incremental_decoding_utils import with_incremental_state
  from fairseq.modules.fairseq_dropout import FairseqDropout
  from torch import nn, Tensor
label_desc: Use image node_id 4 for calling the LunarMultiheadAttention obj's underlying member method code with example usage: obj.reset_parameters() without return types