class_docstr: The label embedding and masking layer from the `"Masked Label
  Prediction: Unified Message Passing Model for Semi-Supervised
  Classification" <https://arxiv.org/abs/2009.03509>`_ paper.
  
  Here, node labels :obj:`y` are merged to the initial node features :obj:`x`
  for a subset of their nodes according to :obj:`mask`.
  
  .. note::
  
      For an example of using :class:`MaskLabel`, see
      `examples/unimp_arxiv.py <https://github.com/pyg-team/
      pytorch_geometric/blob/master/examples/unimp_arxiv.py>`_.
  
  
  Args:
      num_classes (int): The number of classes.
      out_channels (int): Size of each output sample.
      method (str, optional): If set to :obj:`"add"`, label embeddings are
          added to the input. If set to :obj:`"concat"`, label embeddings are
          concatenated. In case :obj:`method="add"`, then :obj:`out_channels`
          needs to be identical to the input dimensionality of node features.
          (default: :obj:`"add"`)
docstr: Resets all learnable parameters of the module.
docstr_tok: ["Resets","all","learnable","parameters","of","the","module","."]
code: def reset_parameters(self):
      r"""Resets all learnable parameters of the module."""
      self.emb.reset_parameters()
  
file_path: pytorch_geometric/torch_geometric/nn/models/mask_label.py
imports: import torch
  from torch import Tensor
label_desc: Use image node_id 2 for calling the MaskLabel obj's underlying member method code with example usage: obj.reset_parameters() without return types