code: def _plotly_viz_plot_time_series(
      df, col, result=None, decompose=False, title="Time Series"
  ):
      """Create timeseries visualization.
  
      Args:
          df: The dataframe
          col (str or [str]): Column of interest. Column datatype must be numerical.
          result: The statsmodels.tsa.seasonal.DecomposeResult object. Defaults to None.
          decompose: Set as True to decompose the timeseries with moving average. result must not be None. Defaults to False.
          title: Title of the plot. Defaults to "Time Series".
  
      Returns:
          The visualization
      """
      if isinstance(col, list):
          data = [go.Scatter(x=df.index, y=df[c], name=c) for c in col]
          ylabel = "Variable" if len(col) > 1 else col[0]
          fig = go.Figure(
              data=data,
              layout=figure_layout(title=title, ylabel=ylabel),
          )
      elif isinstance(col, str) and not decompose:
          fig = go.Figure(
              data=go.Scatter(x=df.index, y=df[col], name=col),
              layout=figure_layout(title=title, ylabel=col),
          )
      elif decompose:
          fig = _plotly_viz_decomposition(result, dates=df.index)
      if _in_notebook():
          init_notebook_mode(connected=True)
          return iplot(fig, config={"displayModeBar": False})
      else:
          return fig
  
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 9 for calling a global function with example usage: _plotly_viz_plot_time_series(df, col, result, decompose, title) and returns: iplot, fig