code: def _generate_offdiag_tapes(
      tape, idx, first_order_recipes, add_unshifted, tapes, coeffs
  ):
      r"""Combine two univariate first order recipes and create
      multi-shifted tapes to compute the off-diagonal entry of the Hessian.
      """
      # pylint: disable=too-many-arguments
  
      recipe_i = first_order_recipes[idx[0]]
      recipe_j = first_order_recipes[idx[1]]
      # The columns of combined_rules contain the coefficients (1), the multipliers (2) and the
      # shifts (2) in that order, with the number in brackets indicating the number of columns
      combined_rules = _combine_shift_rules([recipe_i, recipe_j])
      # If there are unmultiplied, unshifted tapes, the coefficient is memorized and the term
      # removed from the list of tapes to create
      if np.allclose(combined_rules[0, 1:3], 1.0) and np.allclose(
          combined_rules[0, 3:5], 0.0
      ):
          # Extract the unshifted coefficient, if the first shifts (multipliers) equal 0 (1).
          if add_unshifted:
              # Add the unshifted tape if it has not been added yet and is required
              # because f0 was not provided (both captured by add_unshifted).
              tapes.insert(0, tape)
              add_unshifted = False
          unshifted_coeff = combined_rules[0, 0]
          combined_rules = combined_rules[1:]
      else:
          unshifted_coeff = None
  
      s = combined_rules[:, 3:5]
      m = combined_rules[:, 1:3]
      new_tapes = generate_multishifted_tapes(tape, idx, s, m)
      tapes.extend(new_tapes)
      coeffs.append(combined_rules[:, 0])
  
      return add_unshifted, unshifted_coeff
  
imports: import itertools
  import warnings
  from functools import partial
  from typing import Sequence, Callable
  import numpy
  import pennylane
  from pennylane.measurements import ProbabilityMP, StateMP, VarianceMP
  from pennylane.transforms import transform
  from .general_shift_rules import _combine_shift_rules, generate_multishifted_tapes, generate_shifted_tapes
  from .gradient_transform import find_and_validate_gradient_methods
  from .parameter_shift import _get_operation_recipe
  from .hessian_transform import _process_jacs
label_desc: Use image node_id 3 for calling a global function with example usage: _generate_offdiag_tapes(tape, idx, first_order_recipes, add_unshifted, tapes, coeffs) and returns: add_unshifted,  unshifted_coeff