class_docstr: The graph transformer layer from the `"Masked Label Prediction:
  Unified Message Passing Model for Semi-Supervised Classification"
  <https://arxiv.org/abs/2009.03509>`_ paper.
  
  Parameters
  ----------
  in_node_feats : int or pair of ints
      Input feature size. A pair denotes feature sizes of source and
      destination nodes.
  out_node_feats : int
      Output feature size.
  num_heads : int
      Number of multi-head-attentions.
  concat : bool, optional
      If False, the multi-head attentions are averaged instead of concatenated.
      Default: ``True``.
  beta : bool, optional
      If True, use a gated residual connection. Default: ``True``.
  edge_feats: int, optional
      Edge feature size. Default: ``None``.
  bias: bool, optional
      If True, learns a bias term. Default: ``True``.
  root_weight: bool, optional
      If False, will skip to learn a root weight matrix. Default: ``True``.
code: def reset_parameters(self):
      self.lin_key.reset_parameters()
      self.lin_query.reset_parameters()
      self.lin_value.reset_parameters()
      if self.lin_edge is not None:
          self.lin_edge.reset_parameters()
      if self.lin_skip is not None:
          self.lin_skip.reset_parameters()
      if self.lin_beta is not None:
          self.lin_beta.reset_parameters()
  
imports: from typing import Optional, Tuple, Union
  from cugraph_dgl.nn.conv.base import BaseConv, SparseGraph
  from cugraph.utilities.utils import import_optional
label_desc: Use image node_id 2 for calling the TransformerConv obj's underlying member method code with example usage: obj.reset_parameters() without return types