class_docstr: Feature extraction pipeline using no model head. This pipeline extracts the hidden states from the base
  transformer, which can be used as features in downstream tasks.
  
  This feature extraction pipeline can currently be loaded from [`pipeline`] using the task identifier:
  `"feature-extraction"`.
  
  All models may be used for this pipeline. See a list of all models, including community-contributed models on
  [huggingface.co/models](https://huggingface.co/models).
  
  Arguments:
      model ([`PreTrainedModel`] or [`TFPreTrainedModel`]):
          The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
          [`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow.
      tokenizer ([`PreTrainedTokenizer`]):
          The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
          [`PreTrainedTokenizer`].
      modelcard (`str` or [`ModelCard`], *optional*):
          Model card attributed to the model for this pipeline.
      framework (`str`, *optional*):
          The framework to use, either `"pt"` for PyTorch or `"tf"` for TensorFlow. The specified framework must be
          installed.
  
          If no framework is specified, will default to the one currently installed. If no framework is specified and
          both frameworks are installed, will default to the framework of the `model`, or to PyTorch if no model is
          provided.
      return_tensor (`bool`, *optional*):
          If `True`, returns a tensor according to the specified framework, otherwise returns a list.
      task (`str`, defaults to `""`):
          A task-identifier for the pipeline.
      args_parser ([`~pipelines.ArgumentHandler`], *optional*):
          Reference to the object in charge of parsing supplied pipeline parameters.
      device (`int`, *optional*, defaults to -1):
          Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
          the associated CUDA device id.
code: def preprocess(
      self, inputs, **tokenize_kwargs
  ) -> Dict[str, GenericTensor]:
      return_tensors = self.framework
      model_inputs = self.tokenizer(
          inputs, return_tensors=return_tensors, **tokenize_kwargs
      )
      return model_inputs
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/pipelines/feature_extraction.py
imports: from typing import Dict
  from .base import GenericTensor, Pipeline
label_desc: Use image node_id 2 for calling the FeatureExtractionPipeline obj's underlying member method code with example usage: obj.preprocess(inputs) and returns: model_inputs