class_docstr: Maximum independence domain adaptation
  Args:
      n_components (int): Number of components to keep.
      kernel (str): "linear", "rbf", or "poly". Kernel to use for MIDA. Defaults to "linear".
      mu (float): Hyperparameter of the l2 penalty. Defaults to 1.0.
      eta (float): Hyperparameter of the label dependence. Defaults to 1.0.
      augmentation (bool): Whether using covariates as augment features. Defaults to False.
      kernel_params (dict or None): Parameters for the kernel. Defaults to None.
  
  References:
      [1] Yan, K., Kou, L. and Zhang, D., 2018. Learning domain-invariant subspace using domain features and
          independence maximization. IEEE transactions on cybernetics, 48(1), pp.288-299.
docstr: Args:
      x : array-like, shape (n_samples, n_features)
      covariates : array-like, augmentation features, shape (n_samples, n_covariates)
  Returns:
      x_transformed : array-like, shape (n_samples, n_components)
docstr_tok: ["Args",":","x",":","array-like",",","shape","(","n_samples",",","n_features",")","covariates",":","array-like",",","augmentation","features",",","shape","(","n_samples",",","n_covariates",")","Returns",":","x_transformed",":","array-like",",","shape","(","n_samples",",","n_components",")"]
code: def transform(self, x, covariates=None):
      """
      Args:
          x : array-like, shape (n_samples, n_features)
          covariates : array-like, augmentation features, shape (n_samples, n_covariates)
      Returns:
          x_transformed : array-like, shape (n_samples, n_components)
      """
      check_is_fitted(self, "x_fit")
      if isinstance(covariates, np.ndarray) and self.augmentation:
          x = np.concatenate((x, covariates), axis=1)
      kernel_x = self._centerer.transform(
          pairwise_kernels(
              x,
              self.x_fit,
              metric=self.kernel,
              filter_params=True,
              **self.kernel_params,
          )
      )
  
      return np.dot(kernel_x, self.U)
  
imports: import logging
  import warnings
  import numpy
  from numpy.linalg import multi_dot
  from scipy import linalg
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.metrics.pairwise import pairwise_kernels
  from sklearn.preprocessing import KernelCenterer, LabelBinarizer
  from sklearn.utils.validation import check_is_fitted
  from tensorly.base import fold, unfold
  from tensorly.tenalg import multi_mode_dot
label_desc: Use image node_id 6 for calling the MIDA obj's underlying member method code with example usage: obj.transform(x, covariates) and returns: np