code: def linear_ramp_chunk(start, stop, num, dim, step):
      """
      Helper function to find the linear ramp for a chunk.
      """
      num1 = num + 1
  
      shape = list(start.shape)
      shape[dim] = num
      shape = tuple(shape)
  
      dtype = np.dtype(start.dtype)
  
      result = np.empty_like(start, shape=shape, dtype=dtype)
      for i in np.ndindex(start.shape):
          j = list(i)
          j[dim] = slice(None)
          j = tuple(j)
  
          result[j] = np.linspace(start[i], stop, num1, dtype=dtype)[
              1:
          ][::step]
  
      return result
  
imports: from __future__ import annotations
  import itertools
  from collections.abc import Sequence
  from functools import partial
  from itertools import product
  from numbers import Integral, Number
  import numpy
  from tlz import sliding_window
  from dask.array import chunk
  from dask.array.backends import array_creation_dispatch
  from dask.array.core import Array, asarray, block, blockwise, broadcast_arrays, broadcast_to, concatenate, normalize_chunks, stack
  from dask.array.numpy_compat import AxisError
  from dask.array.ufunc import greater_equal, rint
  from dask.array.utils import meta_from_array
  from dask.array.wrap import empty, full, ones, zeros
  from dask.base import tokenize
  from dask.highlevelgraph import HighLevelGraph
  from dask.utils import cached_cumsum, derived_from, is_cupy_type
label_desc: Use image node_id 20 for calling a global function with example usage: linear_ramp_chunk(start, stop, num, dim, step) and returns: result