 Usage  ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline from transformers.pipelines import TextClassificationPipeline  class TextRegressionPipeline(TextClassificationPipeline):     """     Class based on the TextClassificationPipeline from transformers.     The difference is that instead of being based on a classifier, it is based on a regressor.     You can specify the regression threshold when you call the pipeline or when you instantiate the pipeline.     """     def __init__(self, **kwargs):         """         Builds a new Pipeline based on regression.         regression_threshold: Optional(float). If None, the pipeline will simply output the score. If set to a specific value, the output will be both the score and the label.         """         self.regression_threshold = kwargs.pop("regression_threshold", None)         super().__init__(**kwargs)     def __call__(self, *args, **kwargs):         """         You can also specify the regression threshold when you call the pipeline.         regression_threshold: Optional(float). If None, the pipeline will simply output the score. If set to a specific value, the output will be both the score and the label.         """         self.regression_threshold_call = kwargs.pop("regression_threshold", None)         result = super().__call__(*args, **kwargs)         return result     def postprocess(self, model_outputs, function_to_apply=None, return_all_scores=False):         outputs = model_outputs["logits"][0]         outputs = outputs.numpy()         scores = outputs         score = scores[0]         regression_threshold = self.regression_threshold         