code: def percentile(
      a,
      q,
      axis=None,
      out=None,
      overwrite_input=False,
      interpolation="linear",
      keepdims=False,
  ):
      """
      Compute the q-th percentile of the data along the specified axis.
  
      Returns the q-th percentile(s) of the array elements.
  
      Parameters
      ----------
      a : array_like
          Input tensor or object that can be converted to a tensor.
      q : array_like of float
          Percentile or sequence of percentiles to compute, which must be between
          0 and 100 inclusive.
      axis : {int, tuple of int, None}, optional
          Axis or axes along which the percentiles are computed. The
          default is to compute the percentile(s) along a flattened
          version of the tensor.
      out : ndarray, optional
          Alternative output array in which to place the result. It must
          have the same shape and buffer length as the expected output,
          but the type (of the output) will be cast if necessary.
      overwrite_input : bool, optional
          Just for compatibility with Numpy, would not take effect.
      interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
          This optional parameter specifies the interpolation method to
          use when the desired percentile lies between two data points
          ``i < j``:
  
          * 'linear': ``i + (j - i) * fraction``, where ``fraction``
            is the fractional part of the index surrounded by ``i``
            and ``j``.
          * 'lower': ``i``.
          * 'higher': ``j``.
          * 'nearest': ``i`` or ``j``, whichever is nearest.
          * 'midpoint': ``(i + j) / 2``.
      keepdims : bool, optional
          If this is set to True, the axes which are reduced are left in
          the result as dimensions with size one. With this option, the
          result will broadcast correctly against the original array `a`.
  
      Returns
      -------
      percentile : scalar or ndarray
          If `q` is a single percentile and `axis=None`, then the result
          is a scalar. If multiple percentiles are given, first axis of
          the result corresponds to the percentiles. The other axes are
          the axes that remain after the reduction of `a`. If the input
          contains integers or floats smaller than ``float64``, the output
          data-type is ``float64``. Otherwise, the output data-type is the
          same as that of the input. If `out` is specified, that array is
          returned instead.
  
      See Also
      --------
      mean
      median : equivalent to ``percentile(..., 50)``
      nanpercentile
      quantile : equivalent to percentile, except with q in the range [0, 1].
  
      Notes
      -----
      Given a vector ``V`` of length ``N``, the q-th percentile of
      ``V`` is the value ``q/100`` of the way from the minimum to the
      maximum in a sorted copy of ``V``. The values and distances of
      the two nearest neighbors as well as the `interpolation` parameter
      will determine the percentile if the normalized ranking does not
      match the location of ``q`` exactly. This function is the same as
      the median if ``q=50``, the same as the minimum if ``q=0`` and the
      same as the maximum if ``q=100``.
  
      Examples
      --------
      >>> import mars.tensor as mt
      >>> a = mt.array([[10, 7, 4], [3, 2, 1]])
      >>> a.execute()
      array([[10,  7,  4],
             [ 3,  2,  1]])
      >>> mt.percentile(a, 50).execute()
      3.5
      >>> mt.percentile(a, 50, axis=0).execute()
      array([6.5, 4.5, 2.5])
      >>> mt.percentile(a, 50, axis=1).execute()
      array([7.,  2.])
      >>> mt.percentile(a, 50, axis=1, keepdims=True).execute()
      array([[7.],
             [2.]])
  
      >>> m = mt.percentile(a, 50, axis=0)
      >>> out = mt.zeros_like(m)
      >>> mt.percentile(a, 50, axis=0, out=out).execute()
      array([6.5, 4.5, 2.5])
      >>> m.execute()
      array([6.5, 4.5, 2.5])
  
      The different types of interpolation can be visualized graphically:
  
      .. plot::
  
          import matplotlib.pyplot as plt
          import mars.tensor as mt
          import numpy as np
  
          a = mt.arange(4)
          p = mt.linspace(0, 100, 6001)
          ax = plt.gca()
          lines = [
              ('linear', None),
              ('higher', '--'),
              ('lower', '--'),
              ('nearest', '-.'),
              ('midpoint', '-.'),
          ]
          for interpolation, style in lines:
              ax.plot(
                  np.asarray(p), np.asarray(mt.percentile(a, p, interpolation=interpolation)),
                  label=interpolation, linestyle=style)
          ax.set(
              title='Interpolation methods for list: ' + str(a),
              xlabel='Percentile',
              ylabel='List item returned',
              yticks=np.asarray(a))
          ax.legend()
          plt.show()
  
      """
      if not isinstance(q, ENTITY_TYPE):
          q = np.asanyarray(q)
          q = np.true_divide(q, 100)
          # do check instantly if q is not a tensor
          if not _quantile_is_valid(q):
              raise ValueError(q_error_msg)
      else:
          q = truediv(q, 100)
  
      return _quantile_unchecked(
          a,
          q,
          axis=axis,
          out=out,
          overwrite_input=overwrite_input,
          interpolation=interpolation,
          keepdims=keepdims,
          q_error_msg=q_error_msg,
      )
  
imports: import numpy
  from ...core import ENTITY_TYPE
  from ..arithmetic import truediv
  from .quantile import _quantile_unchecked, _quantile_is_valid
label_desc: Use image node_id 1 for calling a global function with example usage: percentile(a, q, axis, out, overwrite_input, interpolation, keepdims) and returns: _quantile_unchecked