code: def get_density(data, xs) -> np.ndarray:
      """Get gaussian kde density to plot.
  
      Parameters
      ----------
      data
          The data used to compute the pdf function.
      xs : iterable
          List of x values to plot the computed pdf for.
      Returns
      -------
      np.array
          The computed pdf values at the points xs.
      """
      # Is only single value adds noise, otherwise there is singular matrix error
      if len(np.unique(data)) == 1:
          data = data + np.random.normal(
              scale=10 * np.finfo(np.float32).eps, size=len(data)
          )
      density = gaussian_kde(data)
      density.covariance_factor = lambda: 0.25
      # pylint: disable=protected-access
      density._compute_covariance()
      return density(xs)
  
file_path: deepchecks/deepchecks/utils/distribution/plot.py
imports: from functools import cmp_to_key
  from numbers import Number
  from typing import Any, Dict, List, Tuple, Union
  import numpy
  import pandas
  import plotly.graph_objs
  from plotly.basedatatypes import BaseTraceType
  from scipy.stats import gaussian_kde
  from typing_extensions import Literal
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.utils.dataframes import un_numpy
  from deepchecks.utils.distribution.preprocessing import preprocess_2_cat_cols_to_same_bins
  from deepchecks.utils.plot import DEFAULT_DATASET_NAMES, colors
label_desc: Use image node_id 1 for calling a global function with example usage: get_density(data, xs) and returns: density