code: def subblocks(a, *ranges):
      """
      This function produces a distributed array from a subset of the blocks in
      the `a`. The result and `a` will have the same number of dimensions. For
      example,
          subblocks(a, [0, 1], [2, 4])
      will produce a DistArray whose object_refs are
          [[a.object_refs[0, 2], a.object_refs[0, 4]],
           [a.object_refs[1, 2], a.object_refs[1, 4]]]
      We allow the user to pass in an empty list [] to indicate the full range.
      """
      ranges = list(ranges)
      if len(ranges) != a.ndim:
          raise Exception(
              "sub_blocks expects to receive a number of ranges "
              "equal to a.ndim, but it received {} ranges and "
              "a.ndim = {}.".format(len(ranges), a.ndim)
          )
      for i in range(len(ranges)):
          # We allow the user to pass in an empty list to indicate the full
          # range.
          if ranges[i] == []:
              ranges[i] = range(a.num_blocks[i])
          if not np.alltrue(ranges[i] == np.sort(ranges[i])):
              raise Exception(
                  "Ranges passed to sub_blocks must be sorted, but "
                  "the {}th range is {}.".format(i, ranges[i])
              )
          if ranges[i][0] < 0:
              raise Exception(
                  "Values in the ranges passed to sub_blocks must "
                  "be at least 0, but the {}th range is {}.".format(
                      i, ranges[i]
                  )
              )
          if ranges[i][-1] >= a.num_blocks[i]:
              raise Exception(
                  "Values in the ranges passed to sub_blocks must "
                  "be less than the relevant number of blocks, but "
                  "the {}th range is {}, and a.num_blocks = {}.".format(
                      i, ranges[i], a.num_blocks
                  )
              )
      last_index = [r[-1] for r in ranges]
      last_block_shape = DistArray.compute_block_shape(
          last_index, a.shape
      )
      shape = [
          (len(ranges[i]) - 1) * BLOCK_SIZE + last_block_shape[i]
          for i in range(a.ndim)
      ]
      result = DistArray(shape)
      for index in np.ndindex(*result.num_blocks):
          result.object_refs[index] = a.object_refs[
              tuple(ranges[i][index[i]] for i in range(a.ndim))
          ]
      return result
  
imports: import numpy
  import ray
  import ray.experimental.array.remote
label_desc: Use image node_id 11 for calling a global function with example usage: subblocks(a) and returns: result