class_docstr: The Mix-Hop graph convolutional operator from the
  `"MixHop: Higher-Order Graph Convolutional Architecturesvia Sparsified
  Neighborhood Mixing" <https://arxiv.org/abs/1905.00067>`_ paper.
  
  .. math::
      \mathbf{X}^{\prime}={\Bigg\Vert}_{p\in P}
      {\left( \mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}}
      \mathbf{\hat{D}}^{-1/2} \right)}^p \mathbf{X} \mathbf{\Theta},
  
  where :math:`\mathbf{\hat{A}} = \mathbf{A} + \mathbf{I}` denotes the
  adjacency matrix with inserted self-loops and
  :math:`\hat{D}_{ii} = \sum_{j=0} \hat{A}_{ij}` its diagonal degree matrix.
  
  Args:
      in_channels (int): Size of each input sample, or :obj:`-1` to derive
          the size from the first input(s) to the forward method.
      out_channels (int): Size of each output sample.
      powers (List[int], optional): The powers of the adjacency matrix to
          use. (default: :obj:`[0, 1, 2]`)
      add_self_loops (bool, optional): If set to :obj:`False`, will not add
          self-loops to the input graph. (default: :obj:`True`)
      bias (bool, optional): If set to :obj:`False`, the layer will not learn
          an additive bias. (default: :obj:`True`)
      **kwargs (optional): Additional arguments of
          :class:`torch_geometric.nn.conv.MessagePassing`.
  
  Shapes:
      - **input:**
        node features :math:`(|\mathcal{V}|, F_{in})`,
        edge indices :math:`(2, |\mathcal{E}|)`,
        edge weights :math:`(|\mathcal{E}|)` *(optional)*
      - **output:**
        node features :math:`(|\mathcal{V}|, |P| \cdot F_{out})`
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code: def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor:
      return spmm(adj_t, x, reduce=self.aggr)
  
file_path: pytorch_geometric/torch_geometric/nn/conv/mixhop_conv.py
imports: from typing import List, Optional
  import torch
  from torch import Tensor
  from torch.nn import Parameter
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.nn.conv.gcn_conv import gcn_norm
  from torch_geometric.nn.dense.linear import Linear
  from torch_geometric.nn.inits import zeros
  from torch_geometric.typing import Adj, OptTensor, SparseTensor
  from torch_geometric.utils import spmm
label_desc: Use image node_id 5 for calling the MixHopConv obj's underlying member method code with example usage: obj.message_and_aggregate(adj_t, x) and returns: spmm