class_docstr: The Neural Fingerprint model from the
  `"Convolutional Networks on Graphs for Learning Molecular Fingerprints"
  <https://arxiv.org/abs/1509.09292>`__ paper to generate fingerprints
  of molecules.
  
  Args:
      in_channels (int): Size of each input sample.
      hidden_channels (int): Size of each hidden sample.
      out_channels (int): Size of each output fingerprint.
      num_layers (int): Number of layers.
      **kwargs (optional): Additional arguments of
          :class:`torch_geometric.nn.conv.MFConv`.
class_docstr_tok: ["The","Neural","Fingerprint","model","from","the","`","``","Convolutional","Networks","on","Graphs","for","Learning","Molecular","Fingerprints","''","<","https",":","\/\/arxiv.org\/abs\/1509.09292",">","`","__","paper","to","generate","fingerprints","of","molecules",".","Args",":","in_channels","(","int",")",":","Size","of","each","input","sample",".","hidden_channels","(","int",")",":","Size","of","each","hidden","sample",".","out_channels","(","int",")",":","Size","of","each","output","fingerprint",".","num_layers","(","int",")",":","Number","of","layers",".","*","*","kwargs","(","optional",")",":","Additional","arguments","of",":","class",":","`","torch_geometric.nn.conv.MFConv","`","."]
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 conv in self.convs:
          conv.reset_parameters()
      for lin in self.lins:
          lin.reset_parameters()
  
file_path: pytorch_geometric/torch_geometric/nn/models/neural_fingerprint.py
imports: from typing import Optional
  import torch
  from torch import Tensor
  from torch_geometric.nn import Linear, MFConv, global_add_pool
  from torch_geometric.typing import Adj
label_desc: Use image node_id 2 for calling the NeuralFingerprint obj's underlying member method code with example usage: obj.reset_parameters() without return types