code: def histogram(
      data,
      range=None,
      bins=50,
      legend=None,
      title=None,
      log=False,
      interactive=False,
  ) -> Union[figure, Tuple[figure, Callable]]:
      """Create a histogram.
  
      Notes
      -----
      `data` can be a :class:`.Float64Expression`, or the result of the :func:`~.aggregators.hist`
      or :func:`~.aggregators.approx_cdf` aggregators.
  
      Parameters
      ----------
      data : :class:`.Struct` or :class:`.Float64Expression`
          Sequence of data to plot.
      range : Tuple[float]
          Range of x values in the histogram.
      bins : int
          Number of bins in the histogram.
      legend : str
          Label of data on the x-axis.
      title : str
          Title of the histogram.
      log : bool
          Plot the log10 of the bin counts.
  
      Returns
      -------
      :class:`bokeh.plotting.figure`
      """
      if isinstance(data, Expression):
          if data._indices.source is not None:
              if interactive:
                  raise ValueError(
                      "'interactive' flag can only be used on data from 'approx_cdf'."
                  )
              agg_f = data._aggregation_method()
              if range is not None:
                  start = range[0]
                  end = range[1]
              else:
                  finite_data = hail.bind(
                      lambda x: hail.case()
                      .when(hail.is_finite(x), x)
                      .or_missing(),
                      data,
                  )
                  start, end = agg_f(
                      (
                          aggregators.min(finite_data),
                          aggregators.max(finite_data),
                      )
                  )
                  if start is None and end is None:
                      raise ValueError(
                          "'data' contains no values that are defined and finite"
                      )
              data = agg_f(aggregators.hist(data, start, end, bins))
          else:
              raise ValueError("Invalid input")
      elif "values" in data:
          cdf = data
          hist, edges = np.histogram(
              cdf["values"],
              bins=bins,
              weights=np.diff(cdf.ranks),
              density=True,
          )
          data = Struct(
              bin_freq=hist, bin_edges=edges, n_larger=0, n_smaller=0
          )
  
      if legend is None:
          legend = ""
  
      if log:
          bin_freq = []
          count_problems = 0
          for x in data.bin_freq:
              if x == 0.0:
                  count_problems += 1
                  bin_freq.append(x)
              else:
                  bin_freq.append(math.log10(x))
  
          if count_problems > 0:
              warning(
                  f"There were {count_problems} bins with height 0, those cannot be log transformed and were left as 0s."
              )
  
          changes = {
              "bin_freq": bin_freq,
              "n_larger": math.log10(data.n_larger)
              if data.n_larger > 0.0
              else data.n_larger,
              "n_smaller": math.log10(data.n_smaller)
              if data.n_smaller > 0.0
              else data.n_smaller,
          }
          data = data.annotate(**changes)
          y_axis_label = "log10 Frequency"
      else:
          y_axis_label = "Frequency"
  
      x_span = data.bin_edges[-1] - data.bin_edges[0]
      x_start = data.bin_edges[0] - 0.05 * x_span
      x_end = data.bin_edges[-1] + 0.05 * x_span
      p = figure(
          title=title,
          x_axis_label=legend,
          y_axis_label=y_axis_label,
          background_fill_color="#EEEEEE",
          x_range=(x_start, x_end),
      )
      q = p.quad(
          bottom=0,
          top=data.bin_freq,
          left=data.bin_edges[:-1],
          right=data.bin_edges[1:],
          legend_label=legend,
          line_color="black",
      )
      if data.n_larger > 0:
          p.quad(
              bottom=0,
              top=data.n_larger,
              left=data.bin_edges[-1],
              right=(
                  data.bin_edges[-1]
                  + (data.bin_edges[1] - data.bin_edges[0])
              ),
              line_color="black",
              fill_color="green",
              legend_label="Outliers Above",
          )
      if data.n_smaller > 0:
          p.quad(
              bottom=0,
              top=data.n_smaller,
              left=data.bin_edges[0]
              - (data.bin_edges[1] - data.bin_edges[0]),
              right=data.bin_edges[0],
              line_color="black",
              fill_color="red",
              legend_label="Outliers Below",
          )
      if interactive:
  
          def mk_interact(handle):
              def update(bins=bins, phase=0):
                  if phase > 0 and phase < 1:
                      bins = bins + 1
                      delta = (
                          cdf["values"][-1] - cdf["values"][0]
                      ) / bins
                      edges = np.linspace(
                          cdf["values"][0] - (1 - phase) * delta,
                          cdf["values"][-1] + phase * delta,
                          bins,
                      )
                  else:
                      edges = np.linspace(
                          cdf["values"][0], cdf["values"][-1], bins
                      )
                  hist, edges = np.histogram(
                      cdf["values"],
                      bins=edges,
                      weights=np.diff(cdf.ranks),
                      density=True,
                  )
                  new_data = {
                      "top": hist,
                      "left": edges[:-1],
                      "right": edges[1:],
                      "bottom": np.full(len(hist), 0),
                  }
                  q.data_source.data = new_data
                  bokeh.io.push_notebook(handle=handle)
  
              from ipywidgets import interact
  
              interact(update, bins=(0, 5 * bins), phase=(0, 1, 0.01))
  
          return p, mk_interact
      else:
          return p
  
imports: import math
  import collections
  import numpy
  import pandas
  import bokeh
  import bokeh.io
  import bokeh.models
  import warnings
  from bokeh.models import HoverTool, ColorBar, LogTicker, LogColorMapper, LinearColorMapper, CategoricalColorMapper, ColumnDataSource, BasicTicker, Plot, CDSView, GroupFilter, IntersectionFilter, Legend, LegendItem, Renderer, CustomJS, Select, Column, Span, DataRange1d, Slope, Label, ColorMapper, GridPlot
  import bokeh.plotting
  import bokeh.palettes
  from bokeh.plotting import figure
  from bokeh.transform import transform
  from bokeh.layouts import gridplot
  from hail.expr import aggregators
  from hail.expr.expressions import Expression, NumericExpression, StringExpression, LocusExpression, Int32Expression, Int64Expression, Float32Expression, Float64Expression, expr_numeric, expr_float64, expr_any, expr_locus, expr_str, raise_unless_row_indexed
  from hail.expr.functions import _error_from_cdf_python
  from hail.typecheck import typecheck, oneof, nullable, sized_tupleof, numeric, sequenceof, dictof
  from hail import Table, MatrixTable
  from hail.utils.struct import Struct
  from hail.utils.java import warning
  from typing import List, Tuple, Dict, Union, Callable, Optional, Sequence, Any, Set
  import hail
label_desc: Use image node_id 7 for calling a global function with example usage: histogram(data, range, bins, legend, title, log, interactive) and returns: p,  mk_interact, p