class_docstr: To analyze the residuals of a given model, we are assuming that $Y=f(X)+\epsilon$ for some unknown
  regression function $f(X)$ we further assume that the error terms $\epsilon_i$ are i.i.d. random variables
  with $\epsilon_i~N(0,\sigma^2)$.
  More precisely we assume:
      1. Zero mean $E[e_i]=0$
      2. Constant variance $Var[e_i]=\sigma^2$
      3. A normal distribution $\epsilon_i~N(0,\sigma^2)$
      4. The error terms are independent
  
  To verify those four assumptions four interactive residual plots will be created.
  If those assumptions are not satisfied, the plausibility of the given model for the given data is to be questioned.
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docstr: Show the plots within the provided Display instance
docstr_tok: ["Show","the","plots","within","the","provided","Display","instance"]
code: def show(self):
      """
      Show the plots within the provided Display instance
      """
      self.display.display(self.plot)
  
imports: from typing import List, Optional
  import numpy
  import pandas
  import plotly.express
  import plotly.graph_objects
  from ipywidgets import Layout, widgets
  from plotly.basewidget import BaseFigureWidget
  from scipy import stats
  import pycaret.internal.plots.helper
  from pycaret.internal.display import CommonDisplay
  from pycaret.internal.display.display_backend import ColabBackend, DatabricksBackend
  from pycaret.internal.logging import get_logger
  from pycaret.internal.validation import fit_if_not_fitted
label_desc: Use image node_id 2 for calling the InteractiveResidualsPlot obj's underlying member method code with example usage: obj.show() without return types