class_docstr: Applies separate linear tranformations to the incoming data dictionary.
  
  For key :math:`\kappa`, it computes
  
  .. math::
      \mathbf{x}^{\prime}_{\kappa} = \mathbf{x}_{\kappa}
      \mathbf{W}^{\top}_{\kappa} + \mathbf{b}_{\kappa}.
  
  It supports lazy initialization and customizable weight and bias
  initialization.
  
  Args:
      in_channels (int or Dict[Any, int]): Size of each input sample. If
          passed an integer, :obj:`types` will be a mandatory argument.
          initialized lazily in case it is given as :obj:`-1`.
      out_channels (int): Size of each output sample.
      types (List[Any], optional): The keys of the input dictionary.
          (default: :obj:`None`)
      **kwargs (optional): Additional arguments of
          :class:`torch_geometric.nn.Linear`.
class_docstr_tok: ["Applies","separate","linear","tranformations","to","the","incoming","data","dictionary",".","For","key",":","math",":","`","\\kappa","`",",","it","computes","..","math",":",":","\\mathbf","{","x","}","^","{","\\prime","}","_","{","\\kappa","}","=","\\mathbf","{","x","}","_","{","\\kappa","}","\\mathbf","{","W","}","^","{","\\top","}","_","{","\\kappa","}","+","\\mathbf","{","b","}","_","{","\\kappa","}",".","It","supports","lazy","initialization","and","customizable","weight","and","bias","initialization",".","Args",":","in_channels","(","int","or","Dict","[","Any",",","int","]",")",":","Size","of","each","input","sample",".","If","passed","an","integer",",",":","obj",":","`","types","`","will","be","a","mandatory","argument",".","initialized","lazily","in","case","it","is","given","as",":","obj",":","`","-1","`",".","out_channels","(","int",")",":","Size","of","each","output","sample",".","types","(","List","[","Any","]",",","optional",")",":","The","keys","of","the","input","dictionary",".","(","default",":",":","obj",":","`","None","`",")","*","*","kwargs","(","optional",")",":","Additional","arguments","of",":","class",":","`","torch_geometric.nn.Linear","`","."]
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."""
      for lin in self.lins.values():
          lin.reset_parameters()
  
imports: import copy
  import math
  from typing import Any, Dict, Optional, Union
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch.nn.parameter import Parameter
  import torch_geometric.backend
  import torch_geometric.typing
  from torch_geometric import is_compiling
  from torch_geometric.nn import inits
  from torch_geometric.typing import pyg_lib
  from torch_geometric.utils import index_sort, scatter
  from torch_geometric.utils.sparse import index2ptr
label_desc: Use image node_id 2 for calling the HeteroDictLinear obj's underlying member method code with example usage: obj.reset_parameters() without return types