class_docstr: A "wrapper" layer designed for heterogeneous graph layers. It takes a
  heterogeneous graph layer, such as :class:`deepsnap.hetero_gnn.HeteroSAGEConv`, 
  at the initializing stage. Currently DeepSNAP does not support `parallelize=True`.
  
  .. note::
  
      For more detailed use of :class:`HeteroConv`, see the `examples/node_classification_hetero 
      <https://github.com/snap-stanford/deepsnap/tree/master/examples/node_classification_hetero>`_ 
      folder.
docstr_tok: []
code: def reset_parameters(self):
      r""" """
      for conv in self.convs.values():
          reset(conv)
  
imports: import collections.abc
  import torch
  import torch_geometric.nn
  import torch_geometric.utils
  import torch.nn
  from torch import Tensor
  from torch_geometric.nn.inits import reset
  from torch_sparse import matmul
  from typing import List, Dict
label_desc: Use image node_id 2 for calling the HeteroConv obj's underlying member method code with example usage: obj.reset_parameters() without return types