class_docstr: Approximate feature map for additive chi2 kernel.
  
  Uses sampling the fourier transform of the kernel characteristic
  at regular intervals.
  
  Since the kernel that is to be approximated is additive, the components of
  the input vectors can be treated separately.  Each entry in the original
  space is transformed into 2*sample_steps+1 features, where sample_steps is
  a parameter of the method. Typical values of sample_steps include 1, 2 and
  3.
  
  Optimal choices for the sampling interval for certain data ranges can be
  computed (see the reference). The default values should be reasonable.
  
  Read more in the :ref:`User Guide <additive_chi_kernel_approx>`.
  
  Parameters
  ----------
  sample_steps : int, optional
      Gives the number of (complex) sampling points.
  sample_interval : float, optional
      Sampling interval. Must be specified when sample_steps not in {1,2,3}.
  
  Notes
  -----
  This estimator approximates a slightly different version of the additive
  chi squared kernel then ``metric.additive_chi2`` computes.
  
  See also
  --------
  SkewedChi2Sampler : A Fourier-approximation to a non-additive variant of
      the chi squared kernel.
  
  sklearn.metrics.pairwise.chi2_kernel : The exact chi squared kernel.
  
  sklearn.metrics.pairwise.additive_chi2_kernel : The exact additive chi
      squared kernel.
  
  References
  ----------
  See `"Efficient additive kernels via explicit feature maps"
  <http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/vedaldi11efficient.pdf>`_
  A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence,
  2011
code: def __init__(self, sample_steps=2, sample_interval=None):
      self.sample_steps = sample_steps
      self.sample_interval = sample_interval
  
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 AdditiveChi2Sampler object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = AdditiveChi2Sampler(sample_steps, sample_interval)