code: def _eq_ragged_scalar(start_indices, flat_array, val):
      """
      Compare elements of a RaggedArray with a scalar array
  
      Parameters
      ----------
      start_indices: ndarray
          start indices of a RaggedArray
      flat_array: ndarray
          flat_array property of a RaggedArray
      val: ndarray
  
      Returns
      -------
      mask: ndarray
          1D bool array of same length as inputs with elements True when
          ragged element equals scalar val, False otherwise.
      """
      n = len(start_indices)
      m = len(flat_array)
      cols = len(val)
      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
  
          if stop_index - start_index != cols:
              el_equal = False
          else:
              el_equal = True
              for val_index, flat_index in enumerate(
                  range(start_index, stop_index)
              ):
                  el_equal &= flat_array[flat_index] == val[val_index]
          result[i] = 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 4 for calling a global function with example usage: _eq_ragged_scalar(start_indices, flat_array, val) and returns: result