code: def ermlpe_interaction(
      h: torch.FloatTensor,
      r: torch.FloatTensor,
      t: torch.FloatTensor,
      mlp: nn.Module,
  ) -> torch.FloatTensor:
      r"""Evaluate the ER-MLPE interaction function.
  
      :param h: shape: (`*batch_dims`, dim)
          The head representations.
      :param r: shape: (`*batch_dims`, dim)
          The relation representations.
      :param t: shape: (`*batch_dims`, dim)
          The tail representations.
      :param mlp:
          The MLP.
  
      :return: shape: batch_dims
          The scores.
      """
      # repeat if necessary, and concat head and relation, (batch_size, num_heads, num_relations, 1, 2 * embedding_dim)
      x = torch.cat(torch.broadcast_tensors(h, r), dim=-1)
  
      # Predict t embedding, shape: (*batch_dims, d)
      *batch_dims, dim = x.shape
      x = mlp(x.view(-1, dim)).view(*batch_dims, -1)
  
      # dot product
      return einsum("...d,...d->...", x, 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 8 for calling a global function with example usage: ermlpe_interaction(h, r, t, mlp) and returns: einsum