class_docstr: Approximates feature map of the "skewed chi-squared" kernel by Monte
  Carlo approximation of its Fourier transform.
  
  Read more in the :ref:`User Guide <skewed_chi_kernel_approx>`.
  
  Parameters
  ----------
  skewedness : float
      "skewedness" parameter of the kernel. Needs to be cross-validated.
  
  n_components : int
      number of Monte Carlo samples per original feature.
      Equals the dimensionality of the computed feature space.
  
  random_state : {int, RandomState}, optional
      If int, random_state is the seed used by the random number generator;
      if RandomState instance, random_state is the random number generator.
  
  References
  ----------
  See "Random Fourier Approximations for Skewed Multiplicative Histogram
  Kernels" by Fuxin Li, Catalin Ionescu and Cristian Sminchisescu.
  
  See also
  --------
  AdditiveChi2Sampler : A different approach for approximating an additive
      variant of the chi squared kernel.
  
  sklearn.metrics.pairwise.chi2_kernel : The exact chi squared kernel.
code: def __init__(
      self, skewedness=1.0, n_components=100, random_state=None
  ):
      self.skewedness = skewedness
      self.n_components = n_components
      self.random_state = random_state
  
imports: import warnings
  import numpy
  import scipy.sparse
  from scipy.linalg import svd
  from .base import BaseEstimator
  from .base import TransformerMixin
  from .utils import check_array, check_random_state, as_float_array
  from .utils.extmath import safe_sparse_dot
  from .utils.validation import check_is_fitted
  from .metrics.pairwise import pairwise_kernels
label_desc: Use image node_id 1 to create a new SkewedChi2Sampler object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = SkewedChi2Sampler(skewedness, n_components, random_state)