code: def cross_e_interaction(
      h: torch.FloatTensor,
      r: torch.FloatTensor,
      c_r: torch.FloatTensor,
      t: torch.FloatTensor,
      bias: torch.FloatTensor,
      activation: nn.Module,
      dropout: Optional[nn.Dropout] = None,
  ) -> torch.FloatTensor:
      r"""
      Evaluate the interaction function of CrossE for the given representations from [zhang2019b]_.
  
      .. math ::
          Dropout(Activation(c_r \odot h + c_r \odot h \odot r + b))^T t)
  
      .. note ::
          The representations have to be in a broadcastable shape.
  
      .. note ::
          The CrossE paper described an additional sigmoid activation as part of the interaction function. Since using a
          log-likelihood loss can cause numerical problems (due to explicitly calling sigmoid before log), we do not
          apply this in our implementation but rather opt for the numerically stable variant. However, the model itself
          has an option ``predict_with_sigmoid``, which can be used to enforce application of sigmoid during inference.
          This can also have an impact of rank-based evaluation, since limited numerical precision can lead to exactly
          equal scores for multiple choices. The definition of a rank is not unambiguous in such case, and there exist
          multiple competing variants how to break the ties. More information on this can be found in the documentation of
          rank-based evaluation.
  
      :param h: shape: (`*batch_dims`, dim)
          The head representations.
      :param r: shape: (`*batch_dims`, dim)
          The relation representations.
      :param c_r: shape: (`*batch_dims`, dim)
          The relation-specific interaction vector.
      :param t: shape: (`*batch_dims`, dim)
          The tail representations.
      :param bias: shape: (dim,)
          The combination bias.
      :param activation:
          The combination activation. Should be :class:`torch.nn.Tanh` for consistency with the CrossE paper.
      :param dropout:
          Dropout applied after the combination.
  
      :return: shape: batch_dims
          The scores.
  
      .. seealso:: https://github.com/wencolani/CrossE
      """
      # head interaction
      h = c_r * h
      # relation interaction (notice that h has been updated)
      r = h * r
      # combination
      x = activation(
          h + r + bias.view(*make_ones_like(h.shape[:-1]), -1)
      )
      if dropout is not None:
          x = dropout(x)
      # similarity
      return (x * t).sum(dim=-1)
  
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 28 for calling a global function with example usage: cross_e_interaction(h, r, c_r, t, bias, activation, dropout) and returns: unknown