code: def mure_interaction(
      h: torch.FloatTensor,
      b_h: torch.FloatTensor,
      r_vec: torch.FloatTensor,
      r_mat: torch.FloatTensor,
      t: torch.FloatTensor,
      b_t: torch.FloatTensor,
      p: Union[int, float, str] = 2,
      power_norm: bool = False,
  ) -> torch.FloatTensor:
      r"""Evaluate the MuRE interaction function from [balazevic2019b]_.
  
      .. math ::
          -\|Rh + r - t\| + b_h + b_t
  
      :param h: shape: (`*batch_dims`, dim)
          The head representations.
      :param b_h: shape: batch_dims
          The head entity bias.
      :param r_vec: shape: (`*batch_dims`, dim)
          The relation vector.
      :param r_mat: shape: (`*batch_dims`, dim,)
          The diagonal relation matrix.
      :param t: shape: (`*batch_dims`, dim)
          The tail representations.
      :param b_t: shape: batch_dims
          The tail entity bias.
      :param p:
          The parameter p for selecting the norm, cf. :func:`torch.linalg.vector_norm`.
      :param power_norm:
          Whether to return the powered norm instead.
  
      :return: shape: batch_dims
          The scores.
      """
      return (
          negative_norm_of_sum(
              h * r_mat,
              r_vec,
              -t,
              p=p,
              power_norm=power_norm,
          )
          + b_h
          + b_t
      )
  
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 24 for calling a global function with example usage: mure_interaction(h, b_h, r_vec, r_mat, t, b_t, p, power_norm) and returns: unknown