code: def scatter_plots(
      data,
      mode="matrix",
      sample=None,
      threshold=None,
      compute_backend=None,
      viz_backend=None,
      **kwargs,
  ):
      """Scatter plots of numeric data.
  
      Args:
          data: A Pandas data frame
          mode (str): {``diagnostic``, ``matrix``, ``all``} The visualization mode.
  
              * ``diagnostic``: Plots selected by scagnostics (scatter plot diagnostics)
              * ``matrix``: Generate the full scatter plot matrix
              * ``all``: Generate all individual scatter plots
          sample: The sampling method to use. Currently not used.
          threshold: The scatter plot diagnostic threshold value [0,1] for returning a
              plot. Only used with "diagnostic" mode. For example, ``{"Outlying": 0.9}``
              returns plots with outlier metrics above 0.9. See
              ``pyscagnostics.measure_names`` for a list of metrics.
  
              * If a number: Returns all plots where at least one metric is above this threshold
              * If a dictionary: Returns plots where the metric is above its threshold.
          compute_backend: The compute backend
          viz_backend: The vizualization backend
          **kwargs: Passed to the visualization framework
  
      Raises:
          ValueError: Invalid input data type.
  
      Returns:
          Scatter plot.
      """
      if not _is_dataframe(data):
          raise ValueError("Unsupported input data type")
  
      swidget = _get_compute_backend(
          compute_backend, data
      ).compute_scatter_plot(data, mode, sample, threshold, **kwargs)
  
      swidget.compute_backend = compute_backend
      swidget.viz_backend = viz_backend
      return swidget
  
file_path: data-describe/data_describe/core/scatter.py
imports: from itertools import combinations
  import warnings
  import seaborn
  from data_describe._widget import BaseWidget
  from data_describe.config._config import get_option
  from data_describe.compat import _is_dataframe, _requires, _compat
  from data_describe.backends import _get_compute_backend, _get_viz_backend
label_desc: Use image node_id 1 for calling a global function with example usage: scatter_plots(data, mode, sample, threshold, compute_backend, viz_backend) and returns: swidget