code: def _pad_if_necessary(x: torch.Tensor, dim: int) -> torch.Tensor:
      """Apply padding if necessary."""
      padding_dim = dim - x.shape[-1]
      if padding_dim < 0:
          raise ValueError("Cannot have a negative padding")
      if padding_dim == 0:
          return x
      return torch.cat(
          [x, x.new_zeros(*x.shape[:-1], padding_dim)], dim=-1
      )
  
imports: import logging
  import math
  from abc import ABC, abstractmethod
  from typing import Any, Iterable, Mapping, Optional, Sequence
  import torch
  from class_resolver import ClassResolver, Hint, HintOrType, OptionalKwargs
  from class_resolver.contrib.torch import activation_resolver
  from docdata import parse_docdata
  from torch import nn
  from .init import uniform_norm_p1_, xavier_normal_
  from .representation import LowRankRepresentation, Representation
  from .utils import ShapeError, adjacency_tensor_to_stacked_matrix, use_horizontal_stacking
  from .weighting import EdgeWeighting, edge_weight_resolver
  from ..triples import CoreTriplesFactory
  from ..utils import ExtraReprMixin, einsum
label_desc: Use image node_id 2 for calling a global function with example usage: _pad_if_necessary(x, dim) and returns: torch, x