class_docstr: This wraps a transformers pipeline object for easy explanations.
  
  By default transformers pipeline object output lists of dictionaries, not standard
  tensors as expected by SHAP. This class wraps pipelines to make them output nice
  tensor formats.
code: def __call__(self, strings):
      assert not isinstance(
          strings, str
      ), "shap.models.TransformersPipeline expects a list of strings not a single string!"
      output = np.zeros([len(strings)] + list(self.output_shape))
      pipeline_dicts = self.inner_model(list(strings))
      for i, val in enumerate(pipeline_dicts):
          if not isinstance(val, list):
              val = [val]
          for obj in val:
              output[i, self.label2id[obj["label"]]] = (
                  scipy.special.logit(obj["score"])
                  if self.rescale_to_logits
                  else obj["score"]
              )
      return output
  
imports: import numpy
  import scipy.special
  from ._model import Model
label_desc: Use image node_id 2 for calling the TransformersPipeline obj's underlying member method code with example usage: obj.__call__(strings) and returns: output