code: def histogram_bin_edges(a, bins=10, range=None, weights=None):
      r"""
      Function to calculate only the edges of the bins used by the `histogram`
      function.
  
      Parameters
      ----------
      a : array_like
          Input data. The histogram is computed over the flattened array.
      bins : int or sequence of scalars or str, optional
          If `bins` is an int, it defines the number of equal-width
          bins in the given range (10, by default). If `bins` is a
          sequence, it defines the bin edges, including the rightmost
          edge, allowing for non-uniform bin widths.
  
          If `bins` is a string from the list below, `histogram_bin_edges` will use
          the method chosen to calculate the optimal bin width and
          consequently the number of bins (see `Notes` for more detail on
          the estimators) from the data that falls within the requested
          range. While the bin width will be optimal for the actual data
          in the range, the number of bins will be computed to fill the
          entire range, including the empty portions. For visualisation,
          using the 'auto' option is suggested. Weighted data is not
          supported for automated bin size selection.
  
          'auto'
              Minimum bin width between the 'sturges' and 'fd' estimators.
              Provides good all-around performance.
  
          'fd' (Freedman Diaconis Estimator)
              Robust (resilient to outliers) estimator that takes into
              account data variability and data size.
  
          'doane'
              An improved version of Sturges' estimator that works better
              with non-normal datasets.
  
          'scott'
              Less robust estimator that takes into account data variability
              and data size.
  
          'stone'
              Estimator based on leave-one-out cross-validation estimate of
              the integrated squared error. Can be regarded as a generalization
              of Scott's rule.
  
          'rice'
              Estimator does not take variability into account, only data
              size. Commonly overestimates number of bins required.
  
          'sturges'
              R's default method, only accounts for data size. Only
              optimal for gaussian data and underestimates number of bins
              for large non-gaussian datasets.
  
          'sqrt'
              Square root (of data size) estimator, used by Excel and
              other programs for its speed and simplicity.
  
      range : (float, float), optional
          The lower and upper range of the bins.  If not provided, range
          is simply ``(a.min(), a.max())``.  Values outside the range are
          ignored. The first element of the range must be less than or
          equal to the second. `range` affects the automatic bin
          computation as well. While bin width is computed to be optimal
          based on the actual data within `range`, the bin count will fill
          the entire range including portions containing no data.
  
      weights : array_like, optional
          An array of weights, of the same shape as `a`.  Each value in
          `a` only contributes its associated weight towards the bin count
          (instead of 1). This is currently not used by any of the bin estimators,
          but may be in the future.
  
      Returns
      -------
      bin_edges : array of dtype float
          The edges to pass into `histogram`
  
      See Also
      --------
      histogram
  
      Notes
      -----
      The methods to estimate the optimal number of bins are well founded
      in literature, and are inspired by the choices R provides for
      histogram visualisation. Note that having the number of bins
      proportional to :math:`n^{1/3}` is asymptotically optimal, which is
      why it appears in most estimators. These are simply plug-in methods
      that give good starting points for number of bins. In the equations
      below, :math:`h` is the binwidth and :math:`n_h` is the number of
      bins. All estimators that compute bin counts are recast to bin width
      using the `ptp` of the data. The final bin count is obtained from
      ``np.round(np.ceil(range / h))``. The final bin width is often less
      than what is returned by the estimators below.
  
      'auto' (minimum bin width of the 'sturges' and 'fd' estimators)
          A compromise to get a good value. For small datasets the Sturges
          value will usually be chosen, while larger datasets will usually
          default to FD.  Avoids the overly conservative behaviour of FD
          and Sturges for small and large datasets respectively.
          Switchover point is usually :math:`a.size \approx 1000`.
  
      'fd' (Freedman Diaconis Estimator)
          .. math:: h = 2 \frac{IQR}{n^{1/3}}
  
          The binwidth is proportional to the interquartile range (IQR)
          and inversely proportional to cube root of a.size. Can be too
          conservative for small datasets, but is quite good for large
          datasets. The IQR is very robust to outliers.
  
      'scott'
          .. math:: h = \sigma \sqrt[3]{\frac{24 \sqrt{\pi}}{n}}
  
          The binwidth is proportional to the standard deviation of the
          data and inversely proportional to cube root of ``x.size``. Can
          be too conservative for small datasets, but is quite good for
          large datasets. The standard deviation is not very robust to
          outliers. Values are very similar to the Freedman-Diaconis
          estimator in the absence of outliers.
  
      'rice'
          .. math:: n_h = 2n^{1/3}
  
          The number of bins is only proportional to cube root of
          ``a.size``. It tends to overestimate the number of bins and it
          does not take into account data variability.
  
      'sturges'
          .. math:: n_h = \log _{2}(n) + 1
  
          The number of bins is the base 2 log of ``a.size``.  This
          estimator assumes normality of data and is too conservative for
          larger, non-normal datasets. This is the default method in R's
          ``hist`` method.
  
      'doane'
          .. math:: n_h = 1 + \log_{2}(n) +
                          \log_{2}\left(1 + \frac{|g_1|}{\sigma_{g_1}}\right)
  
              g_1 = mean\left[\left(\frac{x - \mu}{\sigma}\right)^3\right]
  
              \sigma_{g_1} = \sqrt{\frac{6(n - 2)}{(n + 1)(n + 3)}}
  
          An improved version of Sturges' formula that produces better
          estimates for non-normal datasets. This estimator attempts to
          account for the skew of the data.
  
      'sqrt'
          .. math:: n_h = \sqrt n
  
          The simplest and fastest estimator. Only takes into account the
          data size.
  
      Examples
      --------
      >>> arr = np.array([0, 0, 0, 1, 2, 3, 3, 4, 5])
      >>> np.histogram_bin_edges(arr, bins='auto', range=(0, 1))
      array([0.  , 0.25, 0.5 , 0.75, 1.  ])
      >>> np.histogram_bin_edges(arr, bins=2)
      array([0. , 2.5, 5. ])
  
      For consistency with histogram, an array of pre-computed bins is
      passed through unmodified:
  
      >>> np.histogram_bin_edges(arr, [1, 2])
      array([1, 2])
  
      This function allows one set of bins to be computed, and reused across
      multiple histograms:
  
      >>> shared_bins = np.histogram_bin_edges(arr, bins='auto')
      >>> shared_bins
      array([0., 1., 2., 3., 4., 5.])
  
      >>> group_id = np.array([0, 1, 1, 0, 1, 1, 0, 1, 1])
      >>> hist_0, _ = np.histogram(arr[group_id == 0], bins=shared_bins)
      >>> hist_1, _ = np.histogram(arr[group_id == 1], bins=shared_bins)
  
      >>> hist_0; hist_1
      array([1, 1, 0, 1, 0])
      array([2, 0, 1, 1, 2])
  
      Which gives more easily comparable results than using separate bins for
      each histogram:
  
      >>> hist_0, bins_0 = np.histogram(arr[group_id == 0], bins='auto')
      >>> hist_1, bins_1 = np.histogram(arr[group_id == 1], bins='auto')
      >>> hist_0; hist_1
      array([1, 1, 1])
      array([2, 1, 1, 2])
      >>> bins_0; bins_1
      array([0., 1., 2., 3.])
      array([0.  , 1.25, 2.5 , 3.75, 5.  ])
  
      """
      a, weights = _ravel_and_check_weights(a, weights)
      bin_edges, _ = _get_bin_edges(a, bins, range, weights)
      return bin_edges
  
imports: import contextlib
  import functools
  import operator
  import warnings
  import numpy
  from numpy._core import overrides
label_desc: Use image node_id 16 for calling a global function with example usage: histogram_bin_edges(a, bins, range, weights) and returns: bin_edges