code: def data_heatmap(
      data,
      missing=False,
      compute_backend=None,
      viz_backend=None,
      **kwargs,
  ) -> HeatmapWidget:
      """Visualizes data patterns in the entire dataset by visualizing as a heatmap.
  
      This feature operates in two modes.
  
      (Default): A data heatmap showing standardized values (bounded to [-3, 3]). This
      visualization is useful for showing unusual, ordered patterns in the data that
      would otherwise be unnoticeable in summary statistics or distribution plots.
  
      Missing: Visualize only missing values.
  
      Args:
          data: A pandas data frame
          missing (bool): If True, show only missing values
          compute_backend: The compute backend.
          viz_backend: The visualization backend.
          **kwargs: Keyword arguments
  
      Returns:
          The data heatmap.
      """
      hwidget = _get_compute_backend(
          compute_backend, data
      ).compute_data_heatmap(data, missing=missing, **kwargs)
      hwidget.viz_backend = viz_backend
      return hwidget
  
imports: from typing import List
  import pandas
  import numpy
  from plotly.offline import init_notebook_mode, iplot
  from matplotlib.figure import Figure
  import seaborn
  import plotly.graph_objs
  from sklearn.preprocessing import StandardScaler
  from data_describe._widget import BaseWidget
  from data_describe.config._config import get_option
  from data_describe.compat import _is_dataframe, _requires, _in_notebook
  from data_describe.backends import _get_viz_backend, _get_compute_backend
label_desc: Use image node_id 1 for calling a global function with example usage: data_heatmap(data, missing, compute_backend, viz_backend) and returns: hwidget