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: A pass-through to ensure that feature visualizers work in Pipelines.
  Subclasses may override this method to actually transform data or to
  call drawing methods. The transformer may also take an optional y
  argument if it is required for either transformation or drawing.
  
  Parameters
  ----------
  X : array-like, shape (n_samples, n_features)
      Feature dataset to be transformed.
  
  y : array-like, shape (n_samples,)
      Dependent target data associated with X, unused.
  
  Returns
  -------
  X : array-like, shape (n_samples, n_features)
      Returns the original dataset,  unmodified.
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code: def transform(self, X, y=None):
      """
      A pass-through to ensure that feature visualizers work in Pipelines.
      Subclasses may override this method to actually transform data or to
      call drawing methods. The transformer may also take an optional y
      argument if it is required for either transformation or drawing.
  
      Parameters
      ----------
      X : array-like, shape (n_samples, n_features)
          Feature dataset to be transformed.
  
      y : array-like, shape (n_samples,)
          Dependent target data associated with X, unused.
  
      Returns
      -------
      X : array-like, shape (n_samples, n_features)
          Returns the original dataset,  unmodified.
      """
      return X
  
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 2 for calling the FeatureVisualizer obj's underlying member method code with example usage: obj.transform(X, y) and returns: X