class_docstr: Combines one or more aggregators and transforms its output with one or
  more scalers as introduced in the `"Principal Neighbourhood Aggregation for
  Graph Nets" <https://arxiv.org/abs/2004.05718>`_ paper.
  The scalers are normalised by the in-degree of the training set and so must
  be provided at time of construction.
  See :class:`torch_geometric.nn.conv.PNAConv` for more information.
  
  Args:
      aggr (str or [str] or Aggregation): The aggregation scheme to use.
          See :class:`~torch_geometric.nn.conv.MessagePassing` for more
          information.
      scaler (str or list): Set of scaling function identifiers, namely one
          or more of :obj:`"identity"`, :obj:`"amplification"`,
          :obj:`"attenuation"`, :obj:`"linear"` and :obj:`"inverse_linear"`.
      deg (Tensor): Histogram of in-degrees of nodes in the training set,
          used by scalers to normalize.
      train_norm (bool, optional): Whether normalization parameters
          are trainable. (default: :obj:`False`)
      aggr_kwargs (Dict[str, Any], optional): Arguments passed to the
          respective aggregation function in case it gets automatically
          resolved. (default: :obj:`None`)
class_docstr_tok: ["Combines","one","or","more","aggregators","and","transforms","its","output","with","one","or","more","scalers","as","introduced","in","the","`","``","Principal","Neighbourhood","Aggregation","for","Graph","Nets","''","<","https",":","\/\/arxiv.org\/abs\/2004.05718",">","`","_","paper",".","The","scalers","are","normalised","by","the","in-degree","of","the","training","set","and","so","must","be","provided","at","time","of","construction",".","See",":","class",":","`","torch_geometric.nn.conv.PNAConv","`","for","more","information",".","Args",":","aggr","(","str","or","[","str","]","or","Aggregation",")",":","The","aggregation","scheme","to","use",".","See",":","class",":","`","~torch_geometric.nn.conv.MessagePassing","`","for","more","information",".","scaler","(","str","or","list",")",":","Set","of","scaling","function","identifiers",",","namely","one","or","more","of",":","obj",":","`","``","identity","''","`",",",":","obj",":","`","``","amplification","''","`",",",":","obj",":","`","``","attenuation","''","`",",",":","obj",":","`","``","linear","''","`","and",":","obj",":","`","``","inverse_linear","''","`",".","deg","(","Tensor",")",":","Histogram","of","in-degrees","of","nodes","in","the","training","set",",","used","by","scalers","to","normalize",".","train_norm","(","bool",",","optional",")",":","Whether","normalization","parameters","are","trainable",".","(","default",":",":","obj",":","`","False","`",")","aggr_kwargs","(","Dict","[","str",",","Any","]",",","optional",")",":","Arguments","passed","to","the","respective","aggregation","function","in","case","it","gets","automatically","resolved",".","(","default",":",":","obj",":","`","None","`",")"]
code: def reset_parameters(self):
      self.avg_deg_lin.data.fill_(self.init_avg_deg_lin)
      self.avg_deg_log.data.fill_(self.init_avg_deg_log)
  
file_path: pytorch_geometric/torch_geometric/nn/aggr/scaler.py
imports: from typing import Any, Dict, List, Optional, Union
  import torch
  from torch import Tensor
  from torch_geometric.nn.aggr import Aggregation, MultiAggregation
  from torch_geometric.nn.resolver import aggregation_resolver
  from torch_geometric.utils import degree
label_desc: Use image node_id 2 for calling the DegreeScalerAggregation obj's underlying member method code with example usage: obj.reset_parameters() without return types