code: def remove_low_and_no_objects(
      masks, scores, labels, object_mask_threshold, num_labels
  ):
      """
      Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and
      `labels`.
  
      Args:
          masks (`torch.Tensor`):
              A tensor of shape `(num_queries, height, width)`.
          scores (`torch.Tensor`):
              A tensor of shape `(num_queries)`.
          labels (`torch.Tensor`):
              A tensor of shape `(num_queries)`.
          object_mask_threshold (`float`):
              A number between 0 and 1 used to binarize the masks.
      Raises:
          `ValueError`: Raised when the first dimension doesn't match in all input tensors.
      Returns:
          `Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region
          < `object_mask_threshold`.
      """
      if not (masks.shape[0] == scores.shape[0] == labels.shape[0]):
          raise ValueError(
              "mask, scores and labels must have the same shape!"
          )
  
      to_keep = labels.ne(num_labels) & (scores > object_mask_threshold)
  
      return masks[to_keep], scores[to_keep], labels[to_keep]
  
imports: import pathlib
  import warnings
  from typing import Dict, List, Optional, Set, Tuple, Union
  import numpy
  from PIL import Image
  from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
  from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
  from ...utils import TensorType, is_torch_available, logging
label_desc: Use image node_id 7 for calling a global function with example usage: remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels) and returns: masks,  scores,  labels