code: def stat_summary_bin(
      mapping=None,
      *,
      data=None,
      geom=None,
      position=None,
      show_legend=None,
      sampling=None,
      tooltips=None,
      orientation=None,
      fun=None,
      fun_min=None,
      fun_max=None,
      quantiles=None,
      bins=None,
      binwidth=None,
      center=None,
      boundary=None,
      color_by=None,
      fill_by=None,
      **other_args,
  ):
      """
      Display a distribution by dividing variable mapped to x axis into bins
      and applying aggregation functions to each bin.
  
      Parameters
      ----------
      mapping : `FeatureSpec`
          Set of aesthetic mappings created by `aes()` function.
          Aesthetic mappings describe the way that variables in the data are
          mapped to plot "aesthetics".
      data : dict or Pandas or Polars `DataFrame`
          The data to be displayed in this layer. If None, the default, the data
          is inherited from the plot data as specified in the call to ggplot.
      geom : str, default='pointrange'
          The geometry to display the summary stat for this layer, as a string.
      position : str or `FeatureSpec`, default='identity'
          Position adjustment, either as a string ('identity', 'stack', 'dodge', ...),
          or the result of a call to a position adjustment function.
      show_legend : bool, default=True
          False - do not show legend for this layer.
      sampling : `FeatureSpec`
          Result of the call to the `sampling_xxx()` function.
          To prevent any sampling for this layer pass value "none" (string "none").
      tooltips : `layer_tooltips`
          Result of the call to the `layer_tooltips()` function.
          Specify appearance, style and content.
      orientation : str, default='x'
          Specify the axis that the layer's stat and geom should run along.
          Possible values: 'x', 'y'.
      fun : {'count', 'sum', 'mean', 'median', 'min', 'max', 'lq', 'mq', 'uq'}, default='mean'
          Name of function computing stat variable '..y..'.
          Names 'lq', 'mq', 'uq' corresponds to lower, middle and upper quantiles, default=[0.25, 0.5, 0.75].
      fun_min : {'count', 'sum', 'mean', 'median', 'min', 'max', 'lq', 'mq', 'uq'}, default='min'
          Name of function computing stat variable '..ymin..'.
          Names 'lq', 'mq', 'uq' corresponds to lower, middle and upper quantiles, default=[0.25, 0.5, 0.75].
      fun_max : {'count', 'sum', 'mean', 'median', 'min', 'max', 'lq', 'mq', 'uq'}, default='max'
          Name of function computing stat variable '..ymax..'.
          Names 'lq', 'mq', 'uq' corresponds to lower, middle and upper quantiles, default=[0.25, 0.5, 0.75].
      quantiles : list of float, default=[0.25, 0.5, 0.75]
          A list of probabilities defining the quantile functions 'lq', 'mq' and 'uq'.
          Must contain exactly 3 values between 0 and 1.
      bins : int, default=30
          Number of bins. Overridden by `binwidth`.
      binwidth : float
          The width of the bins. The default is to use bin widths that cover
          the range of the data. You should always override this value,
          exploring multiple widths to find the best to illustrate the stories in your data.
      center : float
          Specify x-value to align bin centers to.
      boundary : float
          Specify x-value to align bin boundary (i.e. point between bins) to.
      color_by : {'fill', 'color', 'paint_a', 'paint_b', 'paint_c'}, default='color'
          Define the color aesthetic for the geometry.
      fill_by : {'fill', 'color', 'paint_a', 'paint_b', 'paint_c'}, default='fill'
          Define the fill aesthetic for the geometry.
      other_args
          Other arguments passed on to the layer.
          These are often aesthetics settings used to set an aesthetic to a fixed value,
          like color='red', fill='blue', size=3 or shape=21.
          They may also be parameters to the paired geom/stat.
  
      Returns
      -------
      `LayerSpec`
          Geom object specification.
  
      Notes
      -----
      Computed variables:
  
      - ..y.. : result of calculating of `fun`.
      - ..ymin.. : result of calculating of `fun_min`.
      - ..ymax.. : result of calculating of `fun_max`.
  
      Examples
      --------
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 9
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          n = 100
          np.random.seed(42)
          x = np.random.uniform(size=n)
          y = np.random.normal(size=n)
          ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \\
              stat_summary_bin()
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 10
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          n = 100
          np.random.seed(42)
          x = np.random.uniform(size=n)
          y = np.random.normal(size=n)
          g = np.random.choice(["A", "B"], size=n)
          ggplot({'x': x, 'y': y, 'g': g}, aes(x='x', y='y', fill='g')) + \\
              stat_summary_bin(geom='crossbar', bins=6, fatten=5, position='dodge')
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 9-10
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          n = 100
          np.random.seed(42)
          x = np.random.uniform(size=n)
          y = np.random.normal(size=n)
          ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \\
              stat_summary_bin(fun='mq', fun_min='lq', fun_max='uq', geom='crossbar', \\
                               bins=11, width=1, quantiles=[.05, .5, .95], boundary=0) + \\
              geom_point()
  
      """
      summary_bin_geom = geom if geom is not None else "pointrange"
      return _geom(
          summary_bin_geom,
          mapping=mapping,
          data=data,
          stat="summarybin",
          position=position,
          show_legend=show_legend,
          sampling=sampling,
          tooltips=tooltips,
          orientation=orientation,
          fun=fun,
          fun_min=fun_min,
          fun_max=fun_max,
          quantiles=quantiles,
          bins=bins,
          binwidth=binwidth,
          center=center,
          boundary=boundary,
          color_by=color_by,
          fill_by=fill_by,
          **other_args,
      )
  
imports: from .geom import _geom
label_desc: Use image node_id 2 for calling a global function with example usage: stat_summary_bin(mapping) and returns: _geom