class_docstr: The Graph Multiset Transformer pooling operator from the
  `"Accurate Learning of Graph Representations
  with Graph Multiset Pooling" <https://arxiv.org/abs/2102.11533>`_ paper.
  
  The :class:`GraphMultisetTransformer` aggregates elements into
  :math:`k` representative elements via attention-based pooling, computes the
  interaction among them via :obj:`num_encoder_blocks` self-attention blocks,
  and finally pools the representative elements via attention-based pooling
  into a single cluster.
  
  .. note::
  
      :class:`GraphMultisetTransformer` requires sorted indices :obj:`index`
      as input. Specifically, if you use this aggregation as part of
      :class:`~torch_geometric.nn.conv.MessagePassing`, ensure that
      :obj:`edge_index` is sorted by destination nodes, either by manually
      sorting edge indices via :meth:`~torch_geometric.utils.sort_edge_index`
      or by calling :meth:`torch_geometric.data.Data.sort`.
  
  Args:
      channels (int): Size of each input sample.
      k (int): Number of :math:`k` representative nodes after pooling.
      num_encoder_blocks (int, optional): Number of Set Attention Blocks
          (SABs) between the two pooling blocks. (default: :obj:`1`)
      heads (int, optional): Number of multi-head-attentions.
          (default: :obj:`1`)
      norm (str, optional): If set to :obj:`True`, will apply layer
          normalization. (default: :obj:`False`)
      dropout (float, optional): Dropout probability of attention weights.
          (default: :obj:`0`)
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code: def reset_parameters(self):
      self.pma1.reset_parameters()
      for encoder in self.encoders:
          encoder.reset_parameters()
      self.pma2.reset_parameters()
  
file_path: pytorch_geometric/torch_geometric/nn/aggr/gmt.py
imports: from typing import Optional
  import torch
  from torch import Tensor
  from torch_geometric.experimental import disable_dynamic_shapes
  from torch_geometric.nn.aggr import Aggregation
  from torch_geometric.nn.aggr.utils import PoolingByMultiheadAttention, SetAttentionBlock
label_desc: Use image node_id 2 for calling the GraphMultisetTransformer obj's underlying member method code with example usage: obj.reset_parameters() without return types