code: def _pandas_compute_decompose_timeseries(
      df, col, model: str = "additive", **kwargs
  ):
      """Seasonal decomposition using moving averages.
  
      Note:
          The decomposition object in Modin does not preserve datetime index.
  
      Args:
          df: The dataframe
          col: The col of interest. Must be numeric datatype
          model: Type of seasonal component. Defaults to "additive".
          **kwargs: Keyword arguments
  
      Returns:
          statsmodels.tsa.seasonal.DecomposeResult object
      """
      return _compat["statsmodels.tsa.seasonal"].seasonal_decompose(  # type: ignore
          df[col], model=model, **kwargs
      )
  
imports: import warnings
  from typing import Optional
  import matplotlib.pyplot
  import seaborn
  import pandas
  import numpy
  import plotly.graph_objs
  from plotly.subplots import make_subplots
  from plotly.offline import init_notebook_mode, iplot
  from data_describe.config._config import get_option
  from data_describe.backends import _get_viz_backend, _get_compute_backend
  from data_describe.compat import _is_dataframe, _compat, _requires, _in_notebook
label_desc: Use image node_id 7 for calling a global function with example usage: _pandas_compute_decompose_timeseries(df, col, model) and returns: _compat