class_docstr: Container for distributions.
  
  This class (object) is returned from the ``distribution`` function. The
  attributes documented below can be accessed or extracted.
  
  Attributes:
      input_data: The input data
      spike_value: Measure of the "spikey"ness metric, which diagnoses spikey
          histograms where the tallest bin is ``n`` times taller than the average bin.
      skew_value: Measure of the skewness metric.
      spike_factor: The threshold factor used to diagnose "spikey"ness.
      skew_factor: The threshold factor used to diagnose skew.
docstr: The default display for this output.
  
  Displays a summary of diagnostics.
  
  Args:
      viz_backend (str, optional): The visualization backend.
      **kwargs: Keyword arguments.
docstr_tok: ["The","default","display","for","this","output",".","Displays","a","summary","of","diagnostics",".","Args",":","viz_backend","(","str",",","optional",")",":","The","visualization","backend",".","*","*","kwargs",":","Keyword","arguments","."]
code: def show(self, viz_backend=None, **kwargs):
      """The default display for this output.
  
      Displays a summary of diagnostics.
  
      Args:
          viz_backend (str, optional): The visualization backend.
          **kwargs: Keyword arguments.
      """
      summary_string = """Distribution Summary:
          Skew detected in {} columns.
          Spikey histograms detected in {} columns.
  
          Use the method plot_distribution("column_name") to view plots for each feature.
  
          Example:
              dist = DistributionWidget(data)
              dist.plot_distribution("column1")
          """.format(
          np.sum(self.skew_value > self.skew_factor),
          np.sum(self.spike_value > self.spike_factor),
      )
      print(summary_string)
  
imports: from typing import Dict, Any, Optional
  from matplotlib.figure import Figure
  from matplotlib.gridspec import GridSpec
  import seaborn
  import numpy
  from data_describe.config._config import get_option
  from data_describe.metrics.univariate import spikey, skewed
  from data_describe._widget import BaseWidget
  from data_describe.compat import _is_dataframe
  from data_describe.backends import _get_viz_backend, _get_compute_backend
label_desc: Use image node_id 2 for calling the DistributionWidget obj's underlying member method code with example usage: obj.show(viz_backend) without return types