code: def quantile(
      a,
      q,
      axis=None,
      out=None,
      overwrite_input=False,
      interpolation="linear",
      keepdims=False,
      **kw,
  ):
      """
      Compute the q-th quantile of the data along the specified axis.
  
      Parameters
      ----------
      a : array_like
          Input tensor or object that can be converted to a tensor.
      q : array_like of float
          Quantile or sequence of quantiles to compute, which must be between
          0 and 1 inclusive.
      axis : {int, tuple of int, None}, optional
          Axis or axes along which the quantiles are computed. The
          default is to compute the quantile(s) along a flattened
          version of the tensor.
      out : Tensor, optional
          Alternative output tensor 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 quantile 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 tensor `a`.
  
      Returns
      -------
      quantile : scalar or Tensor
          If `q` is a single quantile and `axis=None`, then the result
          is a scalar. If multiple quantiles are given, first axis of
          the result corresponds to the quantiles. 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 tensor is
          returned instead.
  
      See Also
      --------
      mean
      percentile : equivalent to quantile, but with q in the range [0, 100].
      median : equivalent to ``quantile(..., 0.5)``
      nanquantile
  
      Notes
      -----
      Given a vector ``V`` of length ``N``, the q-th quantile of
      ``V`` is the value ``q`` 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 quantile if the normalized ranking does not
      match the location of ``q`` exactly. This function is the same as
      the median if ``q=0.5``, the same as the minimum if ``q=0.0`` and the
      same as the maximum if ``q=1.0``.
  
      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.quantile(a, 0.5).execute()
      3.5
      >>> mt.quantile(a, 0.5, axis=0).execute()
      array([6.5, 4.5, 2.5])
      >>> mt.quantile(a, 0.5, axis=1).execute()
      array([7.,  2.])
      >>> mt.quantile(a, 0.5, axis=1, keepdims=True).execute()
      array([[7.],
             [2.]])
      >>> m = mt.quantile(a, 0.5, axis=0)
      >>> out = mt.zeros_like(m)
      >>> mt.quantile(a, 0.5, axis=0, out=out).execute()
      array([6.5, 4.5, 2.5])
      >>> m.execute()
      array([6.5, 4.5, 2.5])
      """
  
      handle_non_numeric = kw.pop("handle_non_numeric", None)
      if len(kw) > 0:  # pragma: no cover
          raise TypeError(
              f"quantile() got an unexpected keyword argument '{next(iter(kw))}'"
          )
  
      if not isinstance(q, ENTITY_TYPE):
          q = np.asanyarray(q)
          # do check instantly if q is not a tensor
          if not _quantile_is_valid(q):
              raise ValueError(q_error_msg)
  
      return _quantile_unchecked(
          a,
          q,
          axis=axis,
          out=out,
          overwrite_input=overwrite_input,
          interpolation=interpolation,
          keepdims=keepdims,
          handle_non_numeric=handle_non_numeric,
      )
  
imports: from collections.abc import Iterable
  import numpy
  from ...None import opcodes
  from ...core import ENTITY_TYPE, recursive_tile
  from ...core.context import get_context
  from ...serialization.serializables import KeyField, AnyField, StringField, BoolField
  from ...utils import has_unknown_shape
  from ..datasource import tensor
  from ..base import moveaxis, where
  from ..indexing import take
  from ..arithmetic import isnan, add
  from ..reduction import any
  from ..operands import TensorOperand, TensorOperandMixin
  from ..core import TENSOR_TYPE, TENSOR_CHUNK_TYPE, TensorOrder
  from ..utils import check_out_param
  from ..array_utils import as_same_device, device
  from .core import _ureduce
label_desc: Use image node_id 4 for calling a global function with example usage: quantile(a, q, axis, out, overwrite_input, interpolation, keepdims) and returns: _quantile_unchecked