code: def _eq_ragged_ndarray1d(start_indices, flat_array, a):
      """
      Compare a RaggedArray with a 1D numpy object array of the same length
  
      Parameters
      ----------
      start_indices: ndarray
          start indices of a RaggedArray
      flat_array: ndarray
          flat_array property of a RaggedArray
      a: ndarray
          1D numpy array of same length as ra
  
      Returns
      -------
      mask: ndarray
          1D bool array of same length as input with elements True when
          corresponding elements are equal, False otherwise
  
      Notes
      -----
      This function is not numba accelerated because it, by design, inputs
      a numpy object array
      """
  
      n = len(start_indices)
      m = len(flat_array)
      result = np.zeros(n, dtype=np.bool_)
      for i in range(n):
          start_index = start_indices[i]
          stop_index = start_indices[i + 1] if i < n - 1 else m
          a_val = a[i]
          if (
              a_val is None
              or (np.isscalar(a_val) and np.isnan(a_val))
              or len(a_val) == 0
          ):
              result[i] = start_index == stop_index
          else:
              result[i] = np.array_equal(
                  flat_array[start_index:stop_index], a_val
              )
  
      return result
  
imports: from __future__ import annotations
  import re
  from functools import total_ordering
  from packaging.version import Version
  import numpy
  import pandas
  from numba import jit
  from pandas.api.extensions import ExtensionDtype, ExtensionArray, register_extension_dtype
  from numbers import Integral
  from pandas.api.types import pandas_dtype, is_extension_array_dtype
label_desc: Use image node_id 5 for calling a global function with example usage: _eq_ragged_ndarray1d(start_indices, flat_array, a) and returns: result