code: def estimate_shots(coeffs, variances=None, error=0.0016):
      r"""Estimate the number of measurements required to compute an expectation value with a target
      error.
  
      See also :func:`estimate_error`.
  
      Args:
          coeffs (list[tensor_like]): list of coefficient groups
          variances (list[float]): variances of the Pauli word groups
          error (float): target error in computing the expectation value
  
      Returns:
          int: the number of measurements
  
      **Example**
  
      >>> coeffs = [np.array([-0.32707061, 0.7896887]), np.array([0.18121046])]
      >>> qml.resource.estimate_shots(coeffs)
      419218
  
      .. details::
          :title: Theory
  
          An estimation for the number of measurements :math:`M` required to predict the expectation
          value of an observable :math:`H = \sum_i A_i`, with :math:`A = \sum_j c_j O_j` representing
          a linear combination of Pauli words, can be obtained following Eq. (34) of
          [`PRX Quantum 2, 040320 (2021) <https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.2.040320>`_]
          as
  
          .. math::
  
              M = \frac{\left ( \sum_i \sqrt{\text{Var}(A_i)} \right )^2}{\epsilon^2},
  
          with :math:`\epsilon` and :math:`\text{Var}(A)` denoting the target error in computing
          :math:`\left \langle H \right \rangle` and the variance in computing
          :math:`\left \langle A \right \rangle`, respectively. It has been shown in Eq. (10) of
          [`arXiv:2201.01471v3 <https://arxiv.org/abs/2201.01471v3>`_] that
          the variances can be computed from the covariances between the Pauli words as
  
          .. math::
  
              \text{Var}(A_i) = \sum_{jk} c_j c_k \text{Cov}(O_j, O_k),
  
          where
  
          .. math::
  
              \text{Cov}(O_j, O_k) = \left \langle O_j O_k \right \rangle - \left \langle O_j \right \rangle \left \langle O_k \right \rangle.
  
          The values of :math:`\text{Cov}(O_j, O_k)` are not known a priori and should be either
          computed from affordable classical methods, such as the configuration interaction with
          singles and doubles (CISD), or approximated with other methods. If the variances are not
          provided to the function as input, they will be approximated following Eqs. (6-7) of
          [`Phys. Rev. Research 4, 033154, 2022 <https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.4.033154>`_]
          by assuming
          :math:`\text{Cov}(O_j, O_k) =0` for :math:`j \neq k` and using :math:`\text{Var}(O_i) \leq 1`
          from
  
          .. math::
  
              \text{Var}(O_i) = \left \langle O_i^2 \right \rangle - \left \langle O_i \right \rangle^2 = 1 - \left \langle O_i \right \rangle^2.
  
          This approximation gives
  
          .. math::
  
              M \approx \frac{\left ( \sum_i \sqrt{\sum_j c_{ij}^2} \right )^2}{\epsilon^2},
  
          where :math:`i` and :math:`j` run over the observable groups and the Pauli words inside the
          group, respectively.
      """
      if variances:
          return int(
              np.ceil(np.sum(np.sqrt(variances)) ** 2 / error**2)
          )
  
      group_sum = [np.sum(coeff**2) for coeff in coeffs]
      return int(np.ceil(np.sum(np.sqrt(group_sum)) ** 2 / error**2))
  
label_desc: Use image node_id 1 for calling a global function with example usage: estimate_shots(coeffs, variances, error) and returns: int, int