class_docstr: Represent relation-weights as a linear combination of base transformation matrices.
  
  The basis decomposition represents the relation-specific transformation matrices
  as a weighted combination of base matrices, $\{\mathbf{B}_i^l\}_{i=1}^{B}$, i.e.,
  
  .. math::
      \mathbf{W}_r^l = \sum \limits_{b=1}^B \alpha_{rb} \mathbf{B}^l_i
  
  The implementation uses a reshaping of the adjacency tensor into a sparse matrix to support message passing by a
  single sparse matrix multiplication, cf. [thanapalasingam2021]_.
  
  .. seealso ::
      https://github.com/thiviyanT/torch-rgcn/blob/267faffd09a441d902c483a8c130410c72910e90/torch_rgcn/layers.py#L450-L565
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code: def reset_parameters(self):  # noqa: D102
      # note: the only parameters are inside the relation representation module, which has its own reset_parameters
      pass
  
imports: import logging
  import math
  from abc import ABC, abstractmethod
  from typing import Any, Iterable, Mapping, Optional, Sequence
  import torch
  from class_resolver import ClassResolver, Hint, HintOrType, OptionalKwargs
  from class_resolver.contrib.torch import activation_resolver
  from docdata import parse_docdata
  from torch import nn
  from .init import uniform_norm_p1_, xavier_normal_
  from .representation import LowRankRepresentation, Representation
  from .utils import ShapeError, adjacency_tensor_to_stacked_matrix, use_horizontal_stacking
  from .weighting import EdgeWeighting, edge_weight_resolver
  from ..triples import CoreTriplesFactory
  from ..utils import ExtraReprMixin, einsum
label_desc: Use image node_id 5 for calling the BasesDecomposition obj's underlying member method code with example usage: obj.reset_parameters() without return types