code: def scatterviz(
      X,
      y=None,
      ax=None,
      features=None,
      classes=None,
      color=None,
      colormap=None,
      markers=None,
      alpha=1.0,
      **kwargs,
  ):
      """Displays a bivariate scatter plot.
  
      This helper function is a quick wrapper to utilize the ScatterVisualizer
      (Transformer) for one-off analysis.
  
      Parameters
      ----------
  
      X : ndarray or DataFrame of shape n x m
          A matrix of n instances with m features
  
      y : ndarray or Series of length n, default: None
          An array or series of target or class values
  
      ax : matplotlib axes, default: None
          The axes to plot the figure on.
  
      features : list of strings, default: None
          The names of two features or columns.
          More than that will raise an error.
  
      classes : list of strings, default: None
          The names of the classes in the target
  
      color : list or tuple of colors, default: None
          Specify the colors for each individual class
  
      colormap : string or matplotlib cmap, default: None
          Sequential colormap for continuous target
  
      markers : iterable of strings, default: ,+o*vhd
          Matplotlib style markers for points on the scatter plot points
  
      alpha : float, default: 1.0
          Specify a transparency where 1 is completely opaque and 0 is completely
          transparent. This property makes densely clustered points more visible.
  
      Returns
      -------
      viz : ScatterVisualizer
          Returns the fitted, finalized visualizer
      """
      # Instantiate the visualizer
      visualizer = ScatterVisualizer(
          ax=ax,
          features=features,
          classes=classes,
          color=color,
          colormap=colormap,
          markers=markers,
          alpha=alpha,
          **kwargs,
      )
  
      # Fit and transform the visualizer (calls draw)
      visualizer.fit(X, y, **kwargs)
      visualizer.transform(X)
  
      # Return the visualizer object
      return visualizer
  
file_path: yellowbrick/yellowbrick/contrib/scatter.py
imports: import itertools
  import numpy
  from yellowbrick.features.base import DataVisualizer
  from yellowbrick.utils import is_dataframe, is_structured_array
  from yellowbrick.utils import has_ndarray_int_columns
  from yellowbrick.exceptions import YellowbrickValueError
  from yellowbrick.style.colors import resolve_colors
label_desc: Use image node_id 1 for calling a global function with example usage: scatterviz(X, y, ax, features, classes, color, colormap, markers, alpha) and returns: visualizer