code: def labeled_comprehension(
      input,
      labels,
      index,
      func,
      out_dtype,
      default,
      pass_positions=False,
  ):
      """Array resulting from applying ``func`` to each labeled region.
  
      Roughly equivalent to [func(input[labels == i]) for i in index].
  
      Sequentially applies an arbitrary function (that works on array_like input)
      to subsets of an N-D image array specified by `labels` and `index`.
      The option exists to provide the function with positional parameters as the
      second argument.
  
      Args:
          input (cupy.ndarray): Data from which to select `labels` to process.
          labels (cupy.ndarray or None):  Labels to objects in `input`. If not
              None, array must be same shape as `input`. If None, `func` is
              applied to raveled `input`.
          index (int, sequence of ints or None): Subset of `labels` to which to
              apply `func`. If a scalar, a single value is returned. If None,
              `func` is applied to all non-zero values of `labels`.
          func (callable): Python function to apply to `labels` from `input`.
          out_dtype (dtype): Dtype to use for `result`.
          default (int, float or None): Default return value when a element of
              `index` does not exist in `labels`.
          pass_positions (bool, optional): If True, pass linear indices to `func`
              as a second argument.
  
      Returns:
          cupy.ndarray: Result of applying `func` to each of `labels` to `input`
          in `index`.
  
      .. seealso:: :func:`scipy.ndimage.labeled_comprehension`
      """
      as_scalar = cupy.isscalar(index)
      input = cupy.asarray(input)
  
      if pass_positions:
          positions = cupy.arange(input.size).reshape(input.shape)
  
      if labels is None:
          if index is not None:
              raise ValueError("index without defined labels")
          if not pass_positions:
              return func(input.ravel())
          else:
              return func(input.ravel(), positions.ravel())
  
      try:
          input, labels = cupy.broadcast_arrays(input, labels)
      except ValueError:
          raise ValueError(
              "input and labels must have the same shape "
              "(excepting dimensions with width 1)"
          )
  
      if index is None:
          if not pass_positions:
              return func(input[labels > 0])
          else:
              return func(input[labels > 0], positions[labels > 0])
  
      index = cupy.atleast_1d(index)
      if cupy.any(
          index.astype(labels.dtype).astype(index.dtype) != index
      ):
          raise ValueError(
              "Cannot convert index values from <%s> to <%s> "
              "(labels.dtype) without loss of precision"
              % (index.dtype, labels.dtype)
          )
  
      index = index.astype(labels.dtype)
  
      # optimization: find min/max in index, and select those parts of labels,
      #               input, and positions
      lo = index.min()
      hi = index.max()
      mask = (labels >= lo) & (labels <= hi)
  
      # this also ravels the arrays
      labels = labels[mask]
      input = input[mask]
      if pass_positions:
          positions = positions[mask]
  
      # sort everything by labels
      label_order = labels.argsort()
      labels = labels[label_order]
      input = input[label_order]
      if pass_positions:
          positions = positions[label_order]
  
      index_order = index.argsort()
      sorted_index = index[index_order]
  
      def do_map(inputs, output):
          """labels must be sorted"""
          nidx = sorted_index.size
  
          # Find boundaries for each stretch of constant labels
          # This could be faster, but we already paid N log N to sort labels.
          lo = cupy.searchsorted(labels, sorted_index, side="left")
          hi = cupy.searchsorted(labels, sorted_index, side="right")
  
          for i, low, high in zip(range(nidx), lo, hi):
              if low == high:
                  continue
              output[i] = func(*[inp[low:high] for inp in inputs])
  
      if out_dtype == object:
          temp = {i: default for i in range(index.size)}
      else:
          temp = cupy.empty(index.shape, out_dtype)
          if default is None and temp.dtype.kind in "fc":
              default = (
                  numpy.nan
              )  # match NumPy floating-point None behavior
          temp[:] = default
  
      if not pass_positions:
          do_map([input], temp)
      else:
          do_map([input, positions], temp)
  
      if out_dtype == object:
          # use a list of arrays since object arrays are not supported
          index_order = cupy.asnumpy(index_order)
          output = [temp[i] for i in index_order.argsort()]
      else:
          output = cupy.zeros(index.shape, out_dtype)
          output[cupy.asnumpy(index_order)] = temp
      if as_scalar:
          output = output[0]
      return output
  
imports: import warnings
  import numpy
  import cupy
  from cupy import _core
  from cupy import _util
label_desc: Use image node_id 29 for calling a global function with example usage: labeled_comprehension(input, labels, index, func, out_dtype, default, pass_positions) and returns: output, func, func, func, func