code: def require_fields(array, required_dtype):
      """
      Casts a structured array to a new dtype using assignment by field-name.
  
      This function assigns from the old to the new array by name, so the
      value of a field in the output array is the value of the field with the
      same name in the source array. This has the effect of creating a new
      ndarray containing only the fields "required" by the required_dtype.
  
      If a field name in the required_dtype does not exist in the
      input array, that field is created and set to 0 in the output array.
  
      Parameters
      ----------
      a : ndarray
         array to cast
      required_dtype : dtype
         datatype for output array
  
      Returns
      -------
      out : ndarray
          array with the new dtype, with field values copied from the fields in
          the input array with the same name
  
      Examples
      --------
  
      >>> from numpy.lib import recfunctions as rfn
      >>> a = np.ones(4, dtype=[('a', 'i4'), ('b', 'f8'), ('c', 'u1')])
      >>> rfn.require_fields(a, [('b', 'f4'), ('c', 'u1')])
      array([(1., 1), (1., 1), (1., 1), (1., 1)],
        dtype=[('b', '<f4'), ('c', 'u1')])
      >>> rfn.require_fields(a, [('b', 'f4'), ('newf', 'u1')])
      array([(1., 0), (1., 0), (1., 0), (1., 0)],
        dtype=[('b', '<f4'), ('newf', 'u1')])
  
      """
      out = np.empty(array.shape, dtype=required_dtype)
      assign_fields_by_name(out, array)
      return out
  
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 41 for calling a global function with example usage: require_fields(array, required_dtype) and returns: out