code: def padding_to(input_tensor: Tensor, max_len: int = 300) -> Tensor:
      """Pad the first dimension of `input_tensor` to `max_len`.
  
      Args:
          input_tensor (Tensor): The tensor to be padded,
          max_len (int): Padding target size in the first dimension.
              Default: 300
      https://github.com/jshilong/DDQ/blob/ddq_detr/projects/models/utils.py#L19
      Returns:
          Tensor: The tensor padded with the first dimension size `max_len`.
      """
      if max_len is None:
          return input_tensor
      num_padding = max_len - len(input_tensor)
      if input_tensor.dim() > 1:
          padding = input_tensor.new_zeros(
              num_padding,
              *input_tensor.size()[1:],
              dtype=input_tensor.dtype,
          )
      else:
          padding = input_tensor.new_zeros(
              num_padding, dtype=input_tensor.dtype
          )
      output_tensor = torch.cat([input_tensor, padding], dim=0)
      return output_tensor
  
imports: from functools import partial
  from typing import List, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  from mmengine.structures import InstanceData
  from mmengine.utils import digit_version
  from six.moves import map, zip
  from torch import Tensor
  from torch.autograd import Function
  from torch.nn import functional
  from mmdet.structures import SampleList
  from mmdet.structures.bbox import BaseBoxes, get_box_type, stack_boxes
  from mmdet.structures.mask import BitmapMasks, PolygonMasks
  from mmdet.utils import OptInstanceList
label_desc: Use image node_id 24 for calling a global function with example usage: padding_to(input_tensor, max_len) and returns: output_tensor, input_tensor