code: def geom_histogram(
      mapping=None,
      *,
      data=None,
      stat=None,
      position=None,
      show_legend=None,
      sampling=None,
      tooltips=None,
      labels=None,
      orientation=None,
      bins=None,
      binwidth=None,
      center=None,
      boundary=None,
      color_by=None,
      fill_by=None,
      **other_args,
  ):
      """
      Display a 1d distribution by dividing variable mapped to x axis into bins
      and counting the number of observations in 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.
      stat : str, default='bin'
          The statistical transformation to use on the data for this layer, as a string.
          Supported transformations: 'identity' (leaves the data unchanged),
          'count' (counts number of points with same x-axis coordinate),
          'bin' (counts number of points with x-axis coordinate in the same bin),
          'smooth' (performs smoothing - linear default),
          'density' (computes and draws kernel density estimate).
      position : str or `FeatureSpec`, default='stack'
          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.
      labels : `layer_labels`
          Result of the call to the `layer_labels()` function.
          Specify style and content of the annotations.
      orientation : str, default='x'
          Specify the axis that the layer's stat and geom should run along.
          Possible values: 'x', 'y'.
      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
      -----
      `geom_histogram()` displays a 1d distribution by dividing variable
      mapped to x-axis into bins and counting the number of observations in each bin.
  
      Computed variables:
  
      - ..count.. : number of points with x-axis coordinate in the same bin.
      - ..binwidth.. : width of each bin.
  
      `geom_histogram()` understands the following aesthetics mappings:
  
      - x : x-axis value (this value will produce cases or bins for bars).
      - y : y-axis value, default: '..count..'. Alternatively: '..density..'.
      - alpha : transparency level of a layer. Accept values between 0 and 1.
      - color (colour) : color of the geometry lines. String in the following formats: RGB/RGBA (e.g. "rgb(0, 0, 255)"); HEX (e.g. "#0000FF"); color name (e.g. "red"); role name ("pen", "paper" or "brush").
      - fill : fill color. String in the following formats: RGB/RGBA (e.g. "rgb(0, 0, 255)"); HEX (e.g. "#0000FF"); color name (e.g. "red"); role name ("pen", "paper" or "brush").
      - size : line width.
      - weight : used by 'bin' stat to compute weighted sum instead of simple count.
  
      Examples
      --------
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 6
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          np.random.seed(42)
          data = {'x': np.random.normal(size=1000)}
          ggplot(data, aes(x='x')) + geom_histogram()
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 7
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          np.random.seed(42)
          data = {'x': np.random.gamma(2.0, size=1000)}
          ggplot(data, aes(x='x')) + \\
              geom_histogram(aes(color='x', fill='x'), bins=50)
  
      |
  
      .. jupyter-execute::
          :linenos:
          :emphasize-lines: 8-10
  
          import numpy as np
          from lets_plot import *
          LetsPlot.setup_html()
          np.random.seed(42)
          x = np.random.normal(scale=3, size=1000)
          y = 2 * (np.round(x) % 2) - 1
          ggplot({'x': x, 'y': y}) + \\
              geom_histogram(aes(x='x', weight='y'), \\
                             center=0, binwidth=1, \\
                             color='black', fill='gray', size=1)
  
      """
      return _geom(
          "histogram",
          mapping=mapping,
          data=data,
          stat=stat,
          position=position,
          show_legend=show_legend,
          sampling=sampling,
          tooltips=tooltips,
          labels=labels,
          orientation=orientation,
          bins=bins,
          binwidth=binwidth,
          center=center,
          boundary=boundary,
          color_by=color_by,
          fill_by=fill_by,
          **other_args,
      )
  
imports: from lets_plot.geo_data_internals.utils import is_geocoder
  from .core import FeatureSpec, LayerSpec
  from .util import as_annotated_data, is_geo_data_frame, geo_data_frame_to_crs, get_geo_data_frame_meta
label_desc: Use image node_id 6 for calling a global function with example usage: geom_histogram(mapping) and returns: _geom