class_docstr: Text classification pipeline using any `ModelForSequenceClassification`. See the [sequence classification
  examples](../task_summary#sequence-classification) for more information.
  
  This text classification pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"sentiment-analysis"` (for classifying sequences according to positive or negative sentiments).
  
  If multiple classification labels are available (`model.config.num_labels >= 2`), the pipeline will run a softmax
  over the results. If there is a single label, the pipeline will run a sigmoid over the result.
  
  The models that this pipeline can use are models that have been fine-tuned on a sequence classification task. See
  the up-to-date list of available models on
  [huggingface.co/models](https://huggingface.co/models?filter=text-classification).
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code: def _forward(self, model_inputs):
      return self.model(**model_inputs)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/pipelines/text_classification.py
imports: import warnings
  from typing import Dict
  import numpy
  from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
  from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
label_desc: Use image node_id 5 for calling the TextClassificationPipeline obj's underlying member method code with example usage: obj._forward(model_inputs) and returns: self