class_docstr: The UnifiedInformationExplainer for returning explanations for pytorch NN models.
      
class_docstr_tok: ["The","UnifiedInformationExplainer","for","returning","explanations","for","pytorch","NN","models","."]
docstr: Function to plot the global importances
  :param top_words: The tokenized words
  :type top_words: str[]
  :param top_importances: The associated feature importances
  :type top_importances: float[]
  :param label_name: The label predicted
  :type label_name: str
docstr_tok: ["Function","to","plot","the","global","importances",":","param","top_words",":","The","tokenized","words",":","type","top_words",":","str","[","]",":","param","top_importances",":","The","associated","feature","importances",":","type","top_importances",":","float","[","]",":","param","label_name",":","The","label","predicted",":","type","label_name",":","str"]
code: def _plot_global_imp(self, top_words, top_importances, label_name):
      """Function to plot the global importances
      :param top_words: The tokenized words
      :type top_words: str[]
      :param top_importances: The associated feature importances
      :type top_importances: float[]
      :param label_name: The label predicted
      :type label_name: str
      """
  
      plt.figure(figsize=(8, 4))
      plt.title(
          "most important words for class label: " + str(label_name),
          fontsize=18,
      )
      plt.bar(
          range(len(top_importances)),
          top_importances,
          color="b",
          align="center",
      )
      plt.xticks(
          range(len(top_importances)),
          top_words,
          rotation=60,
          fontsize=18,
      )
      plt.show()
  
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 9 for calling the UnifiedInformationExplainer obj's underlying member method code with example usage: obj._plot_global_imp(top_words, top_importances, label_name) without return types