class_docstr: The UnifiedInformationExplainer for returning explanations for pytorch NN models.
      
class_docstr_tok: ["The","UnifiedInformationExplainer","for","returning","explanations","for","pytorch","NN","models","."]
docstr: Currently a placeholder visualize function until python widget is in a working state
  :param max_alpha: max alpha value
  :type max_alpha: float
docstr_tok: ["Currently","a","placeholder","visualize","function","until","python","widget","is","in","a","working","state",":","param","max_alpha",":","max","alpha","value",":","type","max_alpha",":","float"]
code: def _visualize(self, max_alpha=0.5):
      """Currently a placeholder visualize function until python widget is in a working state
      :param max_alpha: max alpha value
      :type max_alpha: float
      """
  
      def html_escape(text):
          return html.escape(text)
  
      self._plot_global_imp(
          self.parsed_sentence[1:-2],
          [0.4 - i for i in self.local_importance_values[1:-2]],
          "positive",
      )
  
      max_alpha = 0.5
      highlighted_text = []
      for i, word in enumerate(self.parsed_sentence):
          # since this is a placeholder function, ignore the numbers below
          weight = 0.55 - (self.local_importance_values[i] * 2)
  
          # make it blue if weight positive
          if weight > 0:
              highlighted_text.append(
                  '<span style="background-color:rgba(135,206,250,'
                  + str(abs(weight) / max_alpha)
                  + ');">'
                  + html_escape(word)
                  + "</span>"
              )
          # red if it's negative
          elif weight < 0:
              highlighted_text.append(
                  '<span style="background-color:rgba(250,0,0,'
                  + str(abs(weight) / max_alpha)
                  + ');">'
                  + html_escape(word)
                  + "</span>"
              )
          else:
              highlighted_text.append(word)
      highlighted_text = highlighted_text[1:-2]
      highlighted_text = " ".join(highlighted_text)
      display(HTML(highlighted_text))
  
imports: import torch
  from torch import nn, optim
  from tqdm import tqdm
  import numpy
  import random
  import html
  import matplotlib.pyplot
  from IPython.core.display import display, HTML
  from interpret_text.experimental.common.base_explainer import _validate_X
  from interpret_text.experimental.common.structured_model_mixin import PureStructuredModelMixin
  from interpret_text.experimental.explanation import _create_local_explanation
  from interpret_text.experimental.common.utils_unified import _get_single_embedding, make_bert_embeddings
label_desc: Use image node_id 8 for calling the UnifiedInformationExplainer obj's underlying member method code with example usage: obj._visualize(max_alpha) and returns: html