class_docstr: Image segmentation pipeline using any `AutoModelForXXXSegmentation`. This pipeline predicts masks of objects and
  their classes.
  
  Example:
  
  ```python
  >>> from transformers import pipeline
  
  >>> segmenter = pipeline(model="facebook/detr-resnet-50-panoptic")
  >>> segments = segmenter("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png")
  >>> len(segments)
  2
  
  >>> segments[0]["label"]
  'bird'
  
  >>> segments[1]["label"]
  'bird'
  
  >>> type(segments[0]["mask"])  # This is a black and white mask showing where is the bird on the original image.
  <class 'PIL.Image.Image'>
  
  >>> segments[0]["mask"].size
  (768, 512)
  ```
  
  
  This image segmentation pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"image-segmentation"`.
  
  See the list of available models on
  [huggingface.co/models](https://huggingface.co/models?filter=image-segmentation).
code: def _forward(self, model_inputs):
      target_size = model_inputs.pop("target_size")
      model_outputs = self.model(**model_inputs)
      model_outputs["target_size"] = target_size
      return model_outputs
  
file_path: transformers/src/transformers/pipelines/image_segmentation.py
imports: from typing import Any, Dict, List, Union
  import numpy
  from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
  from .base import PIPELINE_INIT_ARGS, Pipeline
label_desc: Use image node_id 5 for calling the ImageSegmentationPipeline obj's underlying member method code with example usage: obj._forward(model_inputs) and returns: model_outputs