code: def explain_text(text, probas, target_names=None):
      """
      ExplainText adds support for eli5's LIME based TextExplainer.
      Arguments:
       text (str): Text to explain
       probas (black-box classification pipeline): A function which
                      takes a list of strings (documents) and returns a matrix
                      of shape (n_samples, n_classes) with probability values,
                      i.e. a row per document and a column per output label.
      Returns:
       Nothing. To see plots, go to your W&B run page.
      Example:
       wandb.log({'roc': wandb.plots.ExplainText(text, probas)})
      """
      deprecation_notice()
      eli5 = util.get_module(
          "eli5",
          required="explain_text requires the eli5 library, install with `pip install eli5`",
      )
      if test_missing(text=text, probas=probas):
          # and test_types(proba=proba)):
          wandb.termlog("Visualizing TextExplainer.")
          te = eli5.lime.TextExplainer(random_state=42)
          te.fit(text, probas)
          html = te.show_prediction(target_names=target_names)
          return wandb.Html(html.data)
  
imports: import wandb
  from wandb import util
  from wandb.plots.utils import deprecation_notice, encode_labels, test_missing, test_types
label_desc: Use image node_id 1 for calling a global function with example usage: explain_text(text, probas, target_names) and returns: wandb