class_docstr: Transformer class.
  Args:
      d_model (int): model dimension
      nhead (int): number of heads
      num_glance_enc_layers (int): number of glance encoder layers
      num_gaze_s1_dec_layers (int): number of gaze s1 decoder layers
      num_gaze_s2_dec_layers (int): number of gaze s2 decoder layers
      dim_feedforward (int): dimension of feedforward
      dropout (int): drop out probability
      activation (str): activation
class_docstr_tok: ["Transformer","class",".","Args",":","d_model","(","int",")",":","model","dimension","nhead","(","int",")",":","number","of","heads","num_glance_enc_layers","(","int",")",":","number","of","glance","encoder","layers","num_gaze_s1_dec_layers","(","int",")",":","number","of","gaze","s1","decoder","layers","num_gaze_s2_dec_layers","(","int",")",":","number","of","gaze","s2","decoder","layers","dim_feedforward","(","int",")",":","dimension","of","feedforward","dropout","(","int",")",":","drop","out","probability","activation","(","str",")",":","activation"]
code: def _reset_parameters(self):
      for p in self.parameters():
          if p.dim() > 1:
              nn.init.xavier_uniform_(p)
  
imports: import copy
  import torch
  import torch.nn.functional
  from typing import Optional
  from torch import nn, Tensor
label_desc: Use image node_id 2 for calling the Transformer obj's underlying member method code with example usage: obj._reset_parameters() without return types