class_docstr: Approximates feature map of an RBF kernel by Monte Carlo approximation
  of its Fourier transform.
  
  It implements a variant of Random Kitchen Sinks.[1]
  
  Read more in the :ref:`User Guide <rbf_kernel_approx>`.
  
  Parameters
  ----------
  gamma : float
      Parameter of RBF kernel: exp(-gamma * x^2)
  
  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.
  
  Notes
  -----
  See "Random Features for Large-Scale Kernel Machines" by A. Rahimi and
  Benjamin Recht.
  
  [1] "Weighted Sums of Random Kitchen Sinks: Replacing
  minimization with randomization in learning" by A. Rahimi and
  Benjamin Recht.
  (http://people.eecs.berkeley.edu/~brecht/papers/08.rah.rec.nips.pdf)
code: def __init__(self, gamma=1.0, n_components=100, random_state=None):
      self.gamma = gamma
      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 RBFSampler object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = RBFSampler(gamma, n_components, random_state)