code: def pad_to_length(
      tensor: torch.Tensor,
      length: int,
      pad_value: Union[int, float],
      dim: int = -1,
  ) -> torch.Tensor:
      if tensor.size(dim) >= length:
          return tensor
      else:
          pad_size = list(tensor.shape)
          pad_size[dim] = length - tensor.size(dim)
          return torch.cat(
              [
                  tensor,
                  pad_value
                  * torch.ones(
                      *pad_size,
                      dtype=tensor.dtype,
                      device=tensor.device,
                  ),
              ],
              dim=dim,
          )
  
imports: import random
  import warnings
  from collections import deque
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from torch.nn.utils.rnn import pad_sequence
  from torch.utils.data import IterableDataset
  from transformers import DataCollatorForLanguageModeling, PreTrainedTokenizerBase
  from ..import_utils import is_unsloth_available
label_desc: Use image node_id 3 for calling a global function with example usage: pad_to_length(tensor, length, pad_value, dim) and returns: tensor, torch