code: def find_contours(
      image,
      level=None,
      fully_connected="low",
      positive_orientation="low",
      *,
      mask=None,
  ):
      """Find iso-valued contours in a 2D array for a given level value.
  
      Uses the "marching squares" method to compute a the iso-valued contours of
      the input 2D array for a particular level value. Array values are linearly
      interpolated to provide better precision for the output contours.
  
      Parameters
      ----------
      image : (M, N) ndarray of double
          Input image in which to find contours.
      level : float, optional
          Value along which to find contours in the array. By default, the level
          is set to (max(image) + min(image)) / 2
  
          .. versionchanged:: 0.18
              This parameter is now optional.
      fully_connected : str, {'low', 'high'}
           Indicates whether array elements below the given level value are to be
           considered fully-connected (and hence elements above the value will
           only be face connected), or vice-versa. (See notes below for details.)
      positive_orientation : str, {'low', 'high'}
           Indicates whether the output contours will produce positively-oriented
           polygons around islands of low- or high-valued elements. If 'low' then
           contours will wind counter- clockwise around elements below the
           iso-value. Alternately, this means that low-valued elements are always
           on the left of the contour. (See below for details.)
      mask : (M, N) ndarray of bool or None
          A boolean mask, True where we want to draw contours.
          Note that NaN values are always excluded from the considered region
          (``mask`` is set to ``False`` wherever ``array`` is ``NaN``).
  
      Returns
      -------
      contours : list of (K, 2) ndarrays
          Each contour is a ndarray of ``(row, column)`` coordinates along the contour.
  
      See Also
      --------
      skimage.measure.marching_cubes
  
      Notes
      -----
      The marching squares algorithm is a special case of the marching cubes
      algorithm [1]_.  A simple explanation is available here:
  
      http://users.polytech.unice.fr/~lingrand/MarchingCubes/algo.html
  
      There is a single ambiguous case in the marching squares algorithm: when
      a given ``2 x 2``-element square has two high-valued and two low-valued
      elements, each pair diagonally adjacent. (Where high- and low-valued is
      with respect to the contour value sought.) In this case, either the
      high-valued elements can be 'connected together' via a thin isthmus that
      separates the low-valued elements, or vice-versa. When elements are
      connected together across a diagonal, they are considered 'fully
      connected' (also known as 'face+vertex-connected' or '8-connected'). Only
      high-valued or low-valued elements can be fully-connected, the other set
      will be considered as 'face-connected' or '4-connected'. By default,
      low-valued elements are considered fully-connected; this can be altered
      with the 'fully_connected' parameter.
  
      Output contours are not guaranteed to be closed: contours which intersect
      the array edge or a masked-off region (either where mask is False or where
      array is NaN) will be left open. All other contours will be closed. (The
      closed-ness of a contours can be tested by checking whether the beginning
      point is the same as the end point.)
  
      Contours are oriented. By default, array values lower than the contour
      value are to the left of the contour and values greater than the contour
      value are to the right. This means that contours will wind
      counter-clockwise (i.e. in 'positive orientation') around islands of
      low-valued pixels. This behavior can be altered with the
      'positive_orientation' parameter.
  
      The order of the contours in the output list is determined by the position
      of the smallest ``x,y`` (in lexicographical order) coordinate in the
      contour.  This is a side-effect of how the input array is traversed, but
      can be relied upon.
  
      .. warning::
  
         Array coordinates/values are assumed to refer to the *center* of the
         array element. Take a simple example input: ``[0, 1]``. The interpolated
         position of 0.5 in this array is midway between the 0-element (at
         ``x=0``) and the 1-element (at ``x=1``), and thus would fall at
         ``x=0.5``.
  
      This means that to find reasonable contours, it is best to find contours
      midway between the expected "light" and "dark" values. In particular,
      given a binarized array, *do not* choose to find contours at the low or
      high value of the array. This will often yield degenerate contours,
      especially around structures that are a single array element wide. Instead
      choose a middle value, as above.
  
      References
      ----------
      .. [1] Lorensen, William and Harvey E. Cline. Marching Cubes: A High
             Resolution 3D Surface Construction Algorithm. Computer Graphics
             (SIGGRAPH 87 Proceedings) 21(4) July 1987, p. 163-170).
             :DOI:`10.1145/37401.37422`
  
      Examples
      --------
      >>> a = np.zeros((3, 3))
      >>> a[0, 0] = 1
      >>> a
      array([[1., 0., 0.],
             [0., 0., 0.],
             [0., 0., 0.]])
      >>> find_contours(a, 0.5)
      [array([[0. , 0.5],
             [0.5, 0. ]])]
      """
      if fully_connected not in _param_options:
          raise ValueError(
              'Parameters "fully_connected" must be either '
              '"high" or "low".'
          )
      if positive_orientation not in _param_options:
          raise ValueError(
              'Parameters "positive_orientation" must be either '
              '"high" or "low".'
          )
      if image.shape[0] < 2 or image.shape[1] < 2:
          raise ValueError("Input array must be at least 2x2.")
      if image.ndim != 2:
          raise ValueError("Only 2D arrays are supported.")
      if mask is not None:
          if mask.shape != image.shape:
              raise ValueError(
                  'Parameters "array" and "mask"'
                  " must have same shape."
              )
          if not np.can_cast(mask.dtype, bool, casting="safe"):
              raise TypeError(
                  'Parameter "mask" must be a binary array.'
              )
          mask = mask.astype(np.uint8, copy=False)
      if level is None:
          level = (np.nanmin(image) + np.nanmax(image)) / 2.0
  
      segments = _get_contour_segments(
          image.astype(np.float64),
          float(level),
          fully_connected == "high",
          mask=mask,
      )
      contours = _assemble_contours(segments)
      if positive_orientation == "high":
          contours = [c[::-1] for c in contours]
      return contours
  
imports: import numpy
  from ._find_contours_cy import _get_contour_segments
  from collections import deque
label_desc: Use image node_id 1 for calling a global function with example usage: find_contours(image, level, fully_connected, positive_orientation) and returns: contours