class_docstr: The UnifiedInformationExplainer for returning explanations for pytorch NN models.
      
class_docstr_tok: ["The","UnifiedInformationExplainer","for","returning","explanations","for","pytorch","NN","models","."]
docstr: Calculate and return the sigma
  :return: Sigma values of shape ``[seqLen]``, the ``sigma``.
  :rtype: np.ndarray
docstr_tok: ["Calculate","and","return","the","sigma",":","return",":","Sigma","values","of","shape","``","[","seqLen","]","``",",","the","``","sigma","``",".",":","rtype",":","np.ndarray"]
code: def _get_sigma(self):
      """Calculate and return the sigma
      :return: Sigma values of shape ``[seqLen]``, the ``sigma``.
      :rtype: np.ndarray
      """
      ratios = torch.sigmoid(self.ratio)  # S * 1
      return ratios.detach().cpu().numpy()[:, 0] * self.scale
  
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 6 for calling the UnifiedInformationExplainer obj's underlying member method code with example usage: obj._get_sigma() and returns: unknown