class_docstr: Image To Text pipeline using a `AutoModelForVision2Seq`. This pipeline predicts a caption for a given image.
  
  This image to text pipeline can currently be loaded from pipeline() using the following task identifier:
  "image-to-text".
  
  See the list of available models on
  [huggingface.co/models](https://huggingface.co/models?pipeline_tag=image-to-text).
class_docstr_tok: ["Image","To","Text","pipeline","using","a","`","AutoModelForVision2Seq","`",".","This","pipeline","predicts","a","caption","for","a","given","image",".","This","image","to","text","pipeline","can","currently","be","loaded","from","pipeline","(",")","using","the","following","task","identifier",":","''","image-to-text","''",".","See","the","list","of","available","models","on","[","huggingface.co\/models","]","(","https",":","\/\/huggingface.co\/models","?","pipeline_tag=image-to-text",")","."]
code: def postprocess(self, model_outputs):
      records = []
      for output_ids in model_outputs:
          record = {
              "generated_text": self.tokenizer.decode(
                  output_ids,
                  skip_special_tokens=True,
              )
          }
          records.append(record)
      return records
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/pipelines/image_to_text.py
imports: from typing import List, Union
  from ..utils import add_end_docstrings, is_tf_available, is_torch_available, is_vision_available, logging, requires_backends
  from .base import PIPELINE_INIT_ARGS, Pipeline
label_desc: Use image node_id 6 for calling the ImageToTextPipeline obj's underlying member method code with example usage: obj.postprocess(model_outputs) and returns: records