class_docstr: The black-box zeroth-order optimization attack from Pin-Yu Chen et al. (2018). This attack is a variant of the
  C&W attack which uses ADAM coordinate descent to perform numerical estimation of gradients.
  
  | Paper link: https://arxiv.org/abs/1708.03999
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docstr: Implementation of the ADAM optimizer for coordinate descent.
  
  :param losses: Overall loss.
  :param index: Indices of the coordinates to update.
  :param mean: The mean of the gradient (first moment).
  :param var: The uncentered variance of the gradient (second moment).
  :param current_noise: Current noise.
  :param learning_rate: Learning rate for Adam optimizer.
  :param adam_epochs: Epochs to run the Adam optimizer.
  :param proj: Whether to project the noise to the L_p ball.
  :return: Updated noise for coordinate descent.
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code: def _optimizer_adam_coordinate(
      self,
      losses: np.ndarray,
      index: np.ndarray,
      mean: np.ndarray,
      var: np.ndarray,
      current_noise: np.ndarray,
      learning_rate: float,
      adam_epochs: np.ndarray,
      proj: bool,
  ) -> np.ndarray:
      """
      Implementation of the ADAM optimizer for coordinate descent.
  
      :param losses: Overall loss.
      :param index: Indices of the coordinates to update.
      :param mean: The mean of the gradient (first moment).
      :param var: The uncentered variance of the gradient (second moment).
      :param current_noise: Current noise.
      :param learning_rate: Learning rate for Adam optimizer.
      :param adam_epochs: Epochs to run the Adam optimizer.
      :param proj: Whether to project the noise to the L_p ball.
      :return: Updated noise for coordinate descent.
      """
      beta1, beta2 = 0.9, 0.999
  
      # Estimate grads from loss variation (constant `h` from the paper is fixed to .0001)
      grads = np.array(
          [
              (losses[i] - losses[i + 1]) / (2 * self.variable_h)
              for i in range(0, len(losses), 2)
          ]
      )
  
      # ADAM update
      mean[index] = beta1 * mean[index] + (1 - beta1) * grads
      var[index] = beta2 * var[index] + (1 - beta2) * grads**2
  
      corr = (np.sqrt(1 - np.power(beta2, adam_epochs[index]))) / (
          1 - np.power(beta1, adam_epochs[index])
      )
      orig_shape = current_noise.shape
      current_noise = current_noise.reshape(-1)
      current_noise[index] -= (
          learning_rate
          * corr
          * mean[index]
          / (np.sqrt(var[index]) + 1e-8)
      )
      adam_epochs[index] += 1
  
      if (
          proj
          and hasattr(self.estimator, "clip_values")
          and self.estimator.clip_values is not None
      ):
          clip_min, clip_max = self.estimator.clip_values
          current_noise[index] = np.clip(
              current_noise[index], clip_min, clip_max
          )
  
      return current_noise.reshape(orig_shape)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Tuple, Any, TYPE_CHECKING
  import numpy
  from scipy.ndimage import zoom
  from tqdm.auto import trange
  from art.config import ART_NUMPY_DTYPE
  from art.attacks.attack import EvasionAttack
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification.classifier import ClassifierMixin
  from art.utils import compute_success, get_labels_np_array, check_and_transform_label_format
label_desc: Use image node_id 9 for calling the ZooAttack obj's underlying member method code with example usage: obj._optimizer_adam_coordinate(losses, index, mean, var, current_noise, learning_rate, adam_epochs, proj) and returns: current_noise