class_docstr: Base class for feature visualization.
  
  Feature engineering is primarily conceptualized as a transformation or
  extraction operation, e.g. some raw data is passed through a series of
  transformers and mappings to result in some final dataset which can be
  directly fitted to a model. Therefore feature visualizers are
  transformers and support the sklearn transformer interface by implementing
  a transform method.
  
  Subclasses of the FeatureVisualizer may call draw either from fit or from
  transform but must implement both so that they can be supported in pipeline
  objects. By default, the transform method of the visualizer is just a data
  pass through that ensures the visualizer can be placed into a feature
  extraction workflow.
  
  Parameters
  ----------
  ax : matplotlib.Axes, default: None
      The axis to plot the figure on. If None is passed in the current axes
      will be used (or generated if required).
  
  fig : matplotlib Figure, default: None
      The figure to plot the Visualizer on. If None is passed in the current
      plot will be used (or generated if required).
  
  kwargs : dict
      Any additional keyword arguments to pass to the base Visualizer.
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docstr: Fit, transform, then visualize data in one step.
  
  A helper method similar to ``fit_transform`` that allows you to fit,
  transform, and create a visualization in one simple step. Returns a
  transformed dataset similar to ``fit_transform``.
  
  Parameters
  ----------
  X : array-like, shape (n_samples, n_features)
      Feature dataset for both training and transformation.
  
  y : array-like, shape (n_samples,)
      Dependent target dataset optionally used for training.
  
  kwargs : dict, optional
      Keyword arguments to pass to the ``show()`` method.
  
  Returns
  -------
  Xp : array-like, shape (m_samples, m_features)
      The transformed dataset X prime.
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code: def fit_transform_show(self, X, y=None, **kwargs):
      """Fit, transform, then visualize data in one step.
  
      A helper method similar to ``fit_transform`` that allows you to fit,
      transform, and create a visualization in one simple step. Returns a
      transformed dataset similar to ``fit_transform``.
  
      Parameters
      ----------
      X : array-like, shape (n_samples, n_features)
          Feature dataset for both training and transformation.
  
      y : array-like, shape (n_samples,)
          Dependent target dataset optionally used for training.
  
      kwargs : dict, optional
          Keyword arguments to pass to the ``show()`` method.
  
      Returns
      -------
      Xp : array-like, shape (m_samples, m_features)
          The transformed dataset X prime.
      """
      Xp = self.fit_transform(X, y)
      self.show(**kwargs)
      return Xp
  
imports: import numpy
  import matplotlib
  from yellowbrick.base import Visualizer
  from yellowbrick.utils import is_dataframe
  from yellowbrick.style import resolve_colors
  from yellowbrick.exceptions import NotFitted
  from yellowbrick.utils.target import target_color_type, TargetType
  from yellowbrick.exceptions import YellowbrickKeyError, YellowbrickValueError
  from yellowbrick.style import palettes
  from matplotlib.colors import Normalize
  from sklearn.base import TransformerMixin
label_desc: Use image node_id 3 for calling the FeatureVisualizer obj's underlying member method code with example usage: obj.fit_transform_show(X, y) and returns: Xp