class_docstr: The re-implementation for SchNet from the `"SchNet: A Continuous-filter Convolutional Neural Network for Modeling Quantum Interactions" <https://arxiv.org/abs/1706.08566>`_ paper
  under the 3DGN gramework from `"Spherical Message Passing for 3D Molecular Graphs" <https://openreview.net/forum?id=givsRXsOt9r>`_ paper.
  
  Args:
      energy_and_force (bool, optional): If set to :obj:`True`, will predict energy and take the negative of the derivative of the energy with respect to the atomic positions as predicted forces. (default: :obj:`False`)
      num_layers (int, optional): The number of layers. (default: :obj:`6`)
      hidden_channels (int, optional): Hidden embedding size. (default: :obj:`128`)
      out_channels (int, optional): Output embedding size. (default: :obj:`1`)
      num_filters (int, optional): The number of filters to use. (default: :obj:`128`)
      num_gaussians (int, optional): The number of gaussians :math:`\mu`. (default: :obj:`50`)
      cutoff (float, optional): Cutoff distance for interatomic interactions. (default: :obj:`10.0`).
class_docstr_tok: ["The","re-implementation","for","SchNet","from","the","`","``","SchNet",":","A","Continuous-filter","Convolutional","Neural","Network","for","Modeling","Quantum","Interactions","''","<","https",":","\/\/arxiv.org\/abs\/1706.08566",">","`","_","paper","under","the","3DGN","gramework","from","`","``","Spherical","Message","Passing","for","3D","Molecular","Graphs","''","<","https",":","\/\/openreview.net\/forum","?","id=givsRXsOt9r",">","`","_","paper",".","Args",":","energy_and_force","(","bool",",","optional",")",":","If","set","to",":","obj",":","`","True","`",",","will","predict","energy","and","take","the","negative","of","the","derivative","of","the","energy","with","respect","to","the","atomic","positions","as","predicted","forces",".","(","default",":",":","obj",":","`","False","`",")","num_layers","(","int",",","optional",")",":","The","number","of","layers",".","(","default",":",":","obj",":","`","6","`",")","hidden_channels","(","int",",","optional",")",":","Hidden","embedding","size",".","(","default",":",":","obj",":","`","128","`",")","out_channels","(","int",",","optional",")",":","Output","embedding","size",".","(","default",":",":","obj",":","`","1","`",")","num_filters","(","int",",","optional",")",":","The","number","of","filters","to","use",".","(","default",":",":","obj",":","`","128","`",")","num_gaussians","(","int",",","optional",")",":","The","number","of","gaussians",":","math",":","`","\\mu","`",".","(","default",":",":","obj",":","`","50","`",")","cutoff","(","float",",","optional",")",":","Cutoff","distance","for","interatomic","interactions",".","(","default",":",":","obj",":","`","10.0","`",")","."]
code: def reset_parameters(self):
      self.init_v.reset_parameters()
      for update_e in self.update_es:
          update_e.reset_parameters()
      for update_v in self.update_vs:
          update_v.reset_parameters()
      self.update_u.reset_parameters()
  
imports: from math import pi
  import torch
  import torch.nn.functional
  from torch.nn import Embedding, Sequential, Linear
  from torch_scatter import scatter
  from torch_geometric.nn import radius_graph
label_desc: Use image node_id 2 for calling the SchNet obj's underlying member method code with example usage: obj.reset_parameters() without return types