code: def _apply_optional_bn_to_tensor(
      x: torch.FloatTensor,
      output_dropout: nn.Dropout,
      batch_norm: Optional[nn.BatchNorm1d] = None,
  ) -> torch.FloatTensor:
      """Apply optional batch normalization and dropout layer. Supports multiple batch dimensions."""
      if batch_norm is not None:
          shape = x.shape
          x = x.reshape(-1, shape[-1])
          x = batch_norm(x)
          x = x.view(*shape)
      return output_dropout(x)
  
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 1 for calling a global function with example usage: _apply_optional_bn_to_tensor(x, output_dropout, batch_norm) and returns: output_dropout