code: def merge_arrays(
      seqarrays,
      fill_value=-1,
      flatten=False,
      usemask=False,
      asrecarray=False,
  ):
      """
      Merge arrays field by field.
  
      Parameters
      ----------
      seqarrays : sequence of ndarrays
          Sequence of arrays
      fill_value : {float}, optional
          Filling value used to pad missing data on the shorter arrays.
      flatten : {False, True}, optional
          Whether to collapse nested fields.
      usemask : {False, True}, optional
          Whether to return a masked array or not.
      asrecarray : {False, True}, optional
          Whether to return a recarray (MaskedRecords) or not.
  
      Examples
      --------
      >>> from numpy.lib import recfunctions as rfn
      >>> rfn.merge_arrays((np.array([1, 2]), np.array([10., 20., 30.])))
      array([( 1, 10.), ( 2, 20.), (-1, 30.)],
            dtype=[('f0', '<i8'), ('f1', '<f8')])
  
      >>> rfn.merge_arrays((np.array([1, 2], dtype=np.int64),
      ...         np.array([10., 20., 30.])), usemask=False)
       array([(1, 10.0), (2, 20.0), (-1, 30.0)],
               dtype=[('f0', '<i8'), ('f1', '<f8')])
      >>> rfn.merge_arrays((np.array([1, 2]).view([('a', np.int64)]),
      ...               np.array([10., 20., 30.])),
      ...              usemask=False, asrecarray=True)
      rec.array([( 1, 10.), ( 2, 20.), (-1, 30.)],
                dtype=[('a', '<i8'), ('f1', '<f8')])
  
      Notes
      -----
      * Without a mask, the missing value will be filled with something,
        depending on what its corresponding type:
  
        * ``-1``      for integers
        * ``-1.0``    for floating point numbers
        * ``'-'``     for characters
        * ``'-1'``    for strings
        * ``True``    for boolean values
      * XXX: I just obtained these values empirically
      """
      # Only one item in the input sequence ?
      if len(seqarrays) == 1:
          seqarrays = np.asanyarray(seqarrays[0])
      # Do we have a single ndarray as input ?
      if isinstance(seqarrays, (ndarray, np.void)):
          seqdtype = seqarrays.dtype
          # Make sure we have named fields
          if seqdtype.names is None:
              seqdtype = np.dtype([("", seqdtype)])
          if (
              not flatten
              or _zip_dtype((seqarrays,), flatten=True) == seqdtype
          ):
              # Minimal processing needed: just make sure everything's a-ok
              seqarrays = seqarrays.ravel()
              # Find what type of array we must return
              if usemask:
                  if asrecarray:
                      seqtype = MaskedRecords
                  else:
                      seqtype = MaskedArray
              elif asrecarray:
                  seqtype = recarray
              else:
                  seqtype = ndarray
              return seqarrays.view(dtype=seqdtype, type=seqtype)
          else:
              seqarrays = (seqarrays,)
      else:
          # Make sure we have arrays in the input sequence
          seqarrays = [np.asanyarray(_m) for _m in seqarrays]
      # Find the sizes of the inputs and their maximum
      sizes = tuple(a.size for a in seqarrays)
      maxlength = max(sizes)
      # Get the dtype of the output (flattening if needed)
      newdtype = _zip_dtype(seqarrays, flatten=flatten)
      # Initialize the sequences for data and mask
      seqdata = []
      seqmask = []
      # If we expect some kind of MaskedArray, make a special loop.
      if usemask:
          for a, n in zip(seqarrays, sizes):
              nbmissing = maxlength - n
              # Get the data and mask
              data = a.ravel().__array__()
              mask = ma.getmaskarray(a).ravel()
              # Get the filling value (if needed)
              if nbmissing:
                  fval = _check_fill_value(fill_value, a.dtype)
                  if isinstance(fval, (ndarray, np.void)):
                      if len(fval.dtype) == 1:
                          fval = fval.item()[0]
                          fmsk = True
                      else:
                          fval = np.array(fval, dtype=a.dtype, ndmin=1)
                          fmsk = np.ones((1,), dtype=mask.dtype)
              else:
                  fval = None
                  fmsk = True
              # Store an iterator padding the input to the expected length
              seqdata.append(itertools.chain(data, [fval] * nbmissing))
              seqmask.append(itertools.chain(mask, [fmsk] * nbmissing))
          # Create an iterator for the data
          data = tuple(_izip_records(seqdata, flatten=flatten))
          output = ma.array(
              np.fromiter(data, dtype=newdtype, count=maxlength),
              mask=list(_izip_records(seqmask, flatten=flatten)),
          )
          if asrecarray:
              output = output.view(MaskedRecords)
      else:
          # Same as before, without the mask we don't need...
          for a, n in zip(seqarrays, sizes):
              nbmissing = maxlength - n
              data = a.ravel().__array__()
              if nbmissing:
                  fval = _check_fill_value(fill_value, a.dtype)
                  if isinstance(fval, (ndarray, np.void)):
                      if len(fval.dtype) == 1:
                          fval = fval.item()[0]
                      else:
                          fval = np.array(fval, dtype=a.dtype, ndmin=1)
              else:
                  fval = None
              seqdata.append(itertools.chain(data, [fval] * nbmissing))
          output = np.fromiter(
              tuple(_izip_records(seqdata, flatten=flatten)),
              dtype=newdtype,
              count=maxlength,
          )
          if asrecarray:
              output = output.view(recarray)
      # And we're done...
      return output
  
imports: import itertools
  import numpy
  import numpy.ma
  from numpy import ndarray, recarray
  from numpy.ma import MaskedArray
  from numpy.ma.mrecords import MaskedRecords
  from numpy.core.overrides import array_function_dispatch
  from numpy.lib._iotools import _is_string_like
label_desc: Use image node_id 16 for calling a global function with example usage: merge_arrays(seqarrays, fill_value, flatten, usemask, asrecarray) and returns: output, seqarrays