class_docstr: See :class:`torch_geometric.nn.conv.SAGEConv`.
  
  .. note::
  
      :class:`~torch_geometric.nn.dense.DenseSAGEConv` expects to work on
      binary adjacency matrices.
      If you want to make use of weighted dense adjacency matrices, please
      use :class:`torch_geometric.nn.dense.DenseGraphConv` instead.
class_docstr_tok: ["See",":","class",":","`","torch_geometric.nn.conv.SAGEConv","`",".","..","note",":",":",":","class",":","`","~torch_geometric.nn.dense.DenseSAGEConv","`","expects","to","work","on","binary","adjacency","matrices",".","If","you","want","to","make","use","of","weighted","dense","adjacency","matrices",",","please","use",":","class",":","`","torch_geometric.nn.dense.DenseGraphConv","`","instead","."]
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.lin_rel.reset_parameters()
      self.lin_root.reset_parameters()
  
file_path: pytorch_geometric/torch_geometric/nn/dense/dense_sage_conv.py
imports: import torch
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import Linear
  from torch_geometric.typing import OptTensor
label_desc: Use image node_id 2 for calling the DenseSAGEConv obj's underlying member method code with example usage: obj.reset_parameters() without return types