code: def mitigate_depolarizing_noise(
      K, num_wires, method, use_entries=None
  ):
      r"""Estimate depolarizing noise rate(s) using on the diagonal entries of a kernel
      matrix and mitigate the noise, assuming a global depolarizing noise model.
  
      Args:
          K (array[float]): Noisy kernel matrix.
          num_wires (int): Number of wires/qubits of the quantum embedding kernel.
          method (``'single'`` | ``'average'`` | ``'split_channel'``): Strategy for mitigation
  
              * ``'single'``: An alias for ``'average'`` with ``len(use_entries)=1``.
              * ``'average'``: Estimate a global noise rate based on the average of the diagonal
                entries in ``use_entries``, which need to be measured on the quantum computer.
              * ``'split_channel'``: Estimate individual noise rates per embedding, requiring
                all diagonal entries to be measured on the quantum computer.
          use_entries (array[int]): Diagonal entries to use if method in ``['single', 'average']``.
              If ``None``, defaults to ``[0]`` (``'single'``) or ``range(len(K))`` (``'average'``).
  
      Returns:
          array[float]: Mitigated kernel matrix.
  
      Reference:
          This method is introduced in Section V in
          `arXiv:2105.02276 <https://arxiv.org/abs/2105.02276>`_.
  
      **Example:**
  
      For an example usage of ``mitigate_depolarizing_noise`` please refer to the
      `PennyLane demo on the kernel module <https://github.com/PennyLaneAI/qml/tree/master/demonstrations/tutorial_kernel_based_training.py>`_ or `the postprocessing demo for arXiv:2105.02276 <https://github.com/thubregtsen/qhack/blob/master/paper/post_processing_demo.py>`_.
      """
      dim = 2**num_wires
  
      if method == "single":
          if use_entries is None:
              use_entries = (0,)
  
          if K[use_entries[0], use_entries[0]] <= (1 / dim):
              raise ValueError(
                  "The single noise mitigation method cannot be applied "
                  "as the single diagonal element specified is too small."
              )
  
          diagonal_element = K[use_entries[0], use_entries[0]]
          noise_rate = (1 - diagonal_element) * dim / (dim - 1)
          mitigated_matrix = (K - noise_rate / dim) / (1 - noise_rate)
  
      elif method == "average":
          if use_entries is None:
              diagonal_elements = np.diag(K)
          else:
              diagonal_elements = np.diag(K)[np.array(use_entries)]
  
          if np.mean(diagonal_elements) <= 1 / dim:
              raise ValueError(
                  "The average noise mitigation method cannot be applied "
                  "as the average of the used diagonal terms is too small."
              )
  
          noise_rates = (1 - diagonal_elements) * dim / (dim - 1)
          mean_noise_rate = np.mean(noise_rates)
          mitigated_matrix = (K - mean_noise_rate / dim) / (
              1 - mean_noise_rate
          )
  
      elif method == "split_channel":
          if np.any(np.diag(K) <= 1 / dim):
              raise ValueError(
                  "The split channel noise mitigation method cannot be applied "
                  "to the input matrix as its diagonal terms are too small."
              )
  
          eff_noise_rates = np.clip(
              (1 - np.diag(K)) * dim / (dim - 1), 0.0, 1.0
          )
          noise_rates = 1 - np.sqrt(1 - eff_noise_rates)
          inverse_noise = (
              -np.outer(noise_rates, noise_rates)
              + noise_rates.reshape((1, len(K)))
              + noise_rates.reshape((len(K), 1))
          )
  
          mitigated_matrix = (K - inverse_noise / dim) / (
              1 - inverse_noise
          )
      else:
          raise ValueError(
              "Incorrect noise depolarization mitigation method specified. "
              "Accepted strategies are: 'single', 'average' and 'split_channel'."
          )
  
      return mitigated_matrix
  
imports: import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: mitigate_depolarizing_noise(K, num_wires, method, use_entries) and returns: mitigated_matrix