code: def triple_re_interaction(
      # head
      h: torch.FloatTensor,
      # relation
      r_head: torch.FloatTensor,
      r_mid: torch.FloatTensor,
      r_tail: torch.FloatTensor,
      # tail
      t: torch.FloatTensor,
      # version 2: relation factor offset
      u: Optional[float] = None,
      # extension: negative (power) norm
      p: int = 2,
      power_norm: bool = False,
  ) -> torch.FloatTensor:
      r"""Evaluate the TripleRE interaction function.
  
      .. seealso ::
          :class:`pykeen.nn.modules.TripleREInteraction` for the stateful interaction module
  
      :param h: shape: (`*batch_dims`, rank, dim)
          The head representations.
      :param r_head: shape: (`*batch_dims`, rank, dim)
          The relation-specific head multiplicator representations.
      :param r_mid: shape: (`*batch_dims`, rank, dim)
          The relation representations.
      :param r_tail: shape: (`*batch_dims`, rank, dim)
          The relation-specific tail multiplicator representations.
      :param t: shape: (`*batch_dims`, rank, dim)
          The tail representations.
      :param u:
          the relation factor offset. If u is not None or 0, this corresponds to TripleREv2.
      :param p:
          The p for the norm. cf. :func:`torch.linalg.vector_norm`.
      :param power_norm:
          Whether to return the powered norm.
  
      :return: shape: batch_dims
          The scores.
      """
      # note: normalization should be done from the representations
      # cf. https://github.com/LongYu-360/TripleRE-Add-NodePiece/blob/994216dcb1d718318384368dd0135477f852c6a4/TripleRE%2BNodepiece/ogb_wikikg2/model.py#L317-L328  # noqa: E501
      # version 2
      if u is not None:
          # r_head = r_head + u * torch.ones_like(r_head)
          # r_tail = r_tail + u * torch.ones_like(r_tail)
          r_head = r_head + u
          r_tail = r_tail + u
  
      return negative_norm_of_sum(
          h * r_head,
          -t * r_tail,
          r_mid,
          p=p,
          power_norm=power_norm,
      )
  
imports: from __future__ import annotations
  import functools
  from typing import Optional, Tuple, Union
  import numpy
  import torch
  from torch import broadcast_tensors, nn
  from .compute_kernel import batched_dot
  from .sim import KG2E_SIMILARITIES
  from ..typing import GaussianDistribution
  from ..utils import clamp_norm, einsum, is_cudnn_error, make_ones_like, negative_norm, negative_norm_of_sum, project_entity, tensor_product, tensor_sum
label_desc: Use image node_id 30 for calling a global function with example usage: triple_re_interaction(h, r_head, r_mid, r_tail, t, u, p, power_norm) and returns: negative_norm_of_sum