code: def map_overlap(
      func,
      *args,
      depth=None,
      boundary=None,
      trim=True,
      align_arrays=True,
      allow_rechunk=True,
      **kwargs,
  ):
      """Map a function over blocks of arrays with some overlap
  
      We share neighboring zones between blocks of the array, map a
      function, and then trim away the neighboring strips. If depth is
      larger than any chunk along a particular axis, then the array is
      rechunked.
  
      Note that this function will attempt to automatically determine the output
      array type before computing it, please refer to the ``meta`` keyword argument
      in ``map_blocks`` if you expect that the function will not succeed when
      operating on 0-d arrays.
  
      Parameters
      ----------
      func: function
          The function to apply to each extended block.
          If multiple arrays are provided, then the function should expect to
          receive chunks of each array in the same order.
      args : dask arrays
      depth: int, tuple, dict or list, keyword only
          The number of elements that each block should share with its neighbors
          If a tuple or dict then this can be different per axis.
          If a list then each element of that list must be an int, tuple or dict
          defining depth for the corresponding array in `args`.
          Asymmetric depths may be specified using a dict value of (-/+) tuples.
          Note that asymmetric depths are currently only supported when
          ``boundary`` is 'none'.
          The default value is 0.
      boundary: str, tuple, dict or list, keyword only
          How to handle the boundaries.
          Values include 'reflect', 'periodic', 'nearest', 'none',
          or any constant value like 0 or np.nan.
          If a list then each element must be a str, tuple or dict defining the
          boundary for the corresponding array in `args`.
          The default value is 'reflect'.
      trim: bool, keyword only
          Whether or not to trim ``depth`` elements from each block after
          calling the map function.
          Set this to False if your mapping function already does this for you
      align_arrays: bool, keyword only
          Whether or not to align chunks along equally sized dimensions when
          multiple arrays are provided.  This allows for larger chunks in some
          arrays to be broken into smaller ones that match chunk sizes in other
          arrays such that they are compatible for block function mapping. If
          this is false, then an error will be thrown if arrays do not already
          have the same number of blocks in each dimension.
      allow_rechunk: bool, keyword only
          Allows rechunking, otherwise chunk sizes need to match and core
          dimensions are to consist only of one chunk.
      **kwargs:
          Other keyword arguments valid in ``map_blocks``
  
      Examples
      --------
      >>> import numpy as np
      >>> import dask.array as da
  
      >>> x = np.array([1, 1, 2, 3, 3, 3, 2, 1, 1])
      >>> x = da.from_array(x, chunks=5)
      >>> def derivative(x):
      ...     return x - np.roll(x, 1)
  
      >>> y = x.map_overlap(derivative, depth=1, boundary=0)
      >>> y.compute()
      array([ 1,  0,  1,  1,  0,  0, -1, -1,  0])
  
      >>> x = np.arange(16).reshape((4, 4))
      >>> d = da.from_array(x, chunks=(2, 2))
      >>> d.map_overlap(lambda x: x + x.size, depth=1, boundary='reflect').compute()
      array([[16, 17, 18, 19],
             [20, 21, 22, 23],
             [24, 25, 26, 27],
             [28, 29, 30, 31]])
  
      >>> func = lambda x: x + x.size
      >>> depth = {0: 1, 1: 1}
      >>> boundary = {0: 'reflect', 1: 'none'}
      >>> d.map_overlap(func, depth, boundary).compute()  # doctest: +NORMALIZE_WHITESPACE
      array([[12,  13,  14,  15],
             [16,  17,  18,  19],
             [20,  21,  22,  23],
             [24,  25,  26,  27]])
  
      The ``da.map_overlap`` function can also accept multiple arrays.
  
      >>> func = lambda x, y: x + y
      >>> x = da.arange(8).reshape(2, 4).rechunk((1, 2))
      >>> y = da.arange(4).rechunk(2)
      >>> da.map_overlap(func, x, y, depth=1, boundary='reflect').compute() # doctest: +NORMALIZE_WHITESPACE
      array([[ 0,  2,  4,  6],
             [ 4,  6,  8,  10]])
  
      When multiple arrays are given, they do not need to have the
      same number of dimensions but they must broadcast together.
      Arrays are aligned block by block (just as in ``da.map_blocks``)
      so the blocks must have a common chunk size.  This common chunking
      is determined automatically as long as ``align_arrays`` is True.
  
      >>> x = da.arange(8, chunks=4)
      >>> y = da.arange(8, chunks=2)
      >>> r = da.map_overlap(func, x, y, depth=1, boundary='reflect', align_arrays=True)
      >>> len(r.to_delayed())
      4
  
      >>> da.map_overlap(func, x, y, depth=1, boundary='reflect', align_arrays=False).compute()
      Traceback (most recent call last):
          ...
      ValueError: Shapes do not align {'.0': {2, 4}}
  
      Note also that this function is equivalent to ``map_blocks``
      by default.  A non-zero ``depth`` must be defined for any
      overlap to appear in the arrays provided to ``func``.
  
      >>> func = lambda x: x.sum()
      >>> x = da.ones(10, dtype='int')
      >>> block_args = dict(chunks=(), drop_axis=0)
      >>> da.map_blocks(func, x, **block_args).compute()
      10
      >>> da.map_overlap(func, x, **block_args, boundary='reflect').compute()
      10
      >>> da.map_overlap(func, x, **block_args, depth=1, boundary='reflect').compute()
      12
  
      For functions that may not handle 0-d arrays, it's also possible to specify
      ``meta`` with an empty array matching the type of the expected result. In
      the example below, ``func`` will result in an ``IndexError`` when computing
      ``meta``:
  
      >>> x = np.arange(16).reshape((4, 4))
      >>> d = da.from_array(x, chunks=(2, 2))
      >>> y = d.map_overlap(lambda x: x + x[2], depth=1, boundary='reflect', meta=np.array(()))
      >>> y
      dask.array<_trim, shape=(4, 4), dtype=float64, chunksize=(2, 2), chunktype=numpy.ndarray>
      >>> y.compute()
      array([[ 4,  6,  8, 10],
             [ 8, 10, 12, 14],
             [20, 22, 24, 26],
             [24, 26, 28, 30]])
  
      Similarly, it's possible to specify a non-NumPy array to ``meta``:
  
      >>> import cupy  # doctest: +SKIP
      >>> x = cupy.arange(16).reshape((4, 4))  # doctest: +SKIP
      >>> d = da.from_array(x, chunks=(2, 2))  # doctest: +SKIP
      >>> y = d.map_overlap(lambda x: x + x[2], depth=1, boundary='reflect', meta=cupy.array(()))  # doctest: +SKIP
      >>> y  # doctest: +SKIP
      dask.array<_trim, shape=(4, 4), dtype=float64, chunksize=(2, 2), chunktype=cupy.ndarray>
      >>> y.compute()  # doctest: +SKIP
      array([[ 4,  6,  8, 10],
             [ 8, 10, 12, 14],
             [20, 22, 24, 26],
             [24, 26, 28, 30]])
      """
      # Look for invocation using deprecated single-array signature
      # map_overlap(x, func, depth, boundary=None, trim=True, **kwargs)
      if isinstance(func, Array) and callable(args[0]):
          warnings.warn(
              "The use of map_overlap(array, func, **kwargs) is deprecated since dask 2.17.0 "
              "and will be an error in a future release. To silence this warning, use the syntax "
              "map_overlap(func, array0,[ array1, ...,] **kwargs) instead.",
              FutureWarning,
          )
          sig = ["func", "depth", "boundary", "trim"]
          depth = get(sig.index("depth"), args, depth)
          boundary = get(sig.index("boundary"), args, boundary)
          trim = get(sig.index("trim"), args, trim)
          func, args = args[0], [func]
  
      if not callable(func):
          raise TypeError(
              "First argument must be callable function, not {}\n"
              "Usage:   da.map_overlap(function, x)\n"
              "   or:   da.map_overlap(function, x, y, z)".format(
                  type(func).__name__
              )
          )
      if not all(isinstance(x, Array) for x in args):
          raise TypeError(
              "All variadic arguments must be arrays, not {}\n"
              "Usage:   da.map_overlap(function, x)\n"
              "   or:   da.map_overlap(function, x, y, z)".format(
                  [type(x).__name__ for x in args]
              )
          )
  
      # Coerce depth and boundary arguments to lists of individual
      # specifications for each array argument
      def coerce(xs, arg, fn):
          if not isinstance(arg, list):
              arg = [arg] * len(xs)
          return [fn(x.ndim, a) for x, a in zip(xs, arg)]
  
      depth = coerce(args, depth, coerce_depth)
      boundary = coerce(args, boundary, coerce_boundary)
  
      # Align chunks in each array to a common size
      if align_arrays:
          # Reverse unification order to allow block broadcasting
          inds = [list(reversed(range(x.ndim))) for x in args]
          _, args = unify_chunks(
              *list(concat(zip(args, inds))), warn=False
          )
  
      # Escape to map_blocks if depth is zero (a more efficient computation)
      if all(
          [
              all(depth_val == 0 for depth_val in d.values())
              for d in depth
          ]
      ):
          return map_blocks(func, *args, **kwargs)
  
      for i, x in enumerate(args):
          for j in range(x.ndim):
              if (
                  isinstance(depth[i][j], tuple)
                  and boundary[i][j] != "none"
              ):
                  raise NotImplementedError(
                      "Asymmetric overlap is currently only implemented "
                      "for boundary='none', however boundary for dimension "
                      "{} in array argument {} is {}".format(
                          j, i, boundary[i][j]
                      )
                  )
  
      def assert_int_chunksize(xs):
          assert all(
              type(c) is int for x in xs for cc in x.chunks for c in cc
          )
  
      assert_int_chunksize(args)
      if not trim and "chunks" not in kwargs:
          kwargs["chunks"] = args[0].chunks
      args = [
          overlap(x, depth=d, boundary=b, allow_rechunk=allow_rechunk)
          for x, d, b in zip(args, depth, boundary)
      ]
      assert_int_chunksize(args)
      x = map_blocks(func, *args, **kwargs)
      assert_int_chunksize([x])
      if trim:
          # Find index of array argument with maximum rank and break ties by choosing first provided
          i = sorted(enumerate(args), key=lambda v: (v[1].ndim, -v[0]))[
              -1
          ][0]
          # Trim using depth/boundary setting for array of highest rank
          depth = depth[i]
          boundary = boundary[i]
          # remove any dropped axes from depth and boundary variables
          drop_axis = kwargs.pop("drop_axis", None)
          if drop_axis is not None:
              if isinstance(drop_axis, Number):
                  drop_axis = [drop_axis]
  
              # convert negative drop_axis to equivalent positive value
              ndim_out = max(
                  a.ndim for a in args if isinstance(a, Array)
              )
              drop_axis = [d % ndim_out for d in drop_axis]
  
              kept_axes = tuple(
                  ax
                  for ax in range(args[i].ndim)
                  if ax not in drop_axis
              )
              # note that keys are relabeled to match values in range(x.ndim)
              depth = {n: depth[ax] for n, ax in enumerate(kept_axes)}
              boundary = {
                  n: boundary[ax] for n, ax in enumerate(kept_axes)
              }
          return trim_internal(x, depth, boundary)
      else:
          return x
  
imports: from __future__ import annotations
  import warnings
  from numbers import Integral, Number
  import numpy
  from tlz import concat, get, partial
  from tlz.curried import map
  from dask.array import chunk
  from dask.array.core import Array, concatenate, map_blocks, unify_chunks
  from dask.array.creation import empty_like, full_like
  from dask.array.numpy_compat import normalize_axis_tuple
  from dask.base import tokenize
  from dask.highlevelgraph import HighLevelGraph
  from dask.layers import ArrayOverlapLayer
  from dask.utils import derived_from
label_desc: Use image node_id 15 for calling a global function with example usage: map_overlap(func) and returns: unknown, map_blocks, trim_internal, x