code: def _eq_ragged_ndarray2d(start_indices, flat_array, a):
      """
      Compare a RaggedArray with rows of a 2D numpy object array
  
      Parameters
      ----------
      start_indices: ndarray
          start indices of a RaggedArray
      flat_array: ndarray
          flat_array property of a RaggedArray
      a: ndarray
          A 2D numpy array where the length of the first dimension matches the
          length of the RaggedArray
  
      Returns
      -------
      mask: ndarray
          1D bool array of same length as input RaggedArray with elements True
          when corresponding elements of ra equal corresponding row of `a`
      """
      n = len(start_indices)
      m = len(flat_array)
      cols = a.shape[1]
  
      # np.bool is an alias for Python's built-in bool type, np.bool_ is the
      # numpy type that numba recognizes
      result = np.zeros(n, dtype=np.bool_)
      for row in range(n):
          start_index = start_indices[row]
          stop_index = start_indices[row + 1] if row < n - 1 else m
  
          # Check equality
          if stop_index - start_index != cols:
              el_equal = False
          else:
              el_equal = True
              for col, flat_index in enumerate(
                  range(start_index, stop_index)
              ):
                  el_equal &= flat_array[flat_index] == a[row, col]
          result[row] = el_equal
      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 6 for calling a global function with example usage: _eq_ragged_ndarray2d(start_indices, flat_array, a) and returns: result