class_docstr: Approximate a kernel map using a subset of the training data.
  
  Constructs an approximate feature map for an arbitrary kernel
  using a subset of the data as basis.
  
  Read more in the :ref:`User Guide <nystroem_kernel_approx>`.
  
  Parameters
  ----------
  kernel : string or callable, default="rbf"
      Kernel map to be approximated. A callable should accept two arguments
      and the keyword arguments passed to this object as kernel_params, and
      should return a floating point number.
  
  n_components : int
      Number of features to construct.
      How many data points will be used to construct the mapping.
  
  gamma : float, default=None
      Gamma parameter for the RBF, polynomial, exponential chi2 and
      sigmoid kernels. Interpretation of the default value is left to
      the kernel; see the documentation for sklearn.metrics.pairwise.
      Ignored by other kernels.
  
  degree : float, default=3
      Degree of the polynomial kernel. Ignored by other kernels.
  
  coef0 : float, default=1
      Zero coefficient for polynomial and sigmoid kernels.
      Ignored by other kernels.
  
  kernel_params : mapping of string to any, optional
      Additional parameters (keyword arguments) for kernel function passed
      as callable object.
  
  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.
  
  
  Attributes
  ----------
  components_ : array, shape (n_components, n_features)
      Subset of training points used to construct the feature map.
  
  component_indices_ : array, shape (n_components)
      Indices of ``components_`` in the training set.
  
  normalization_ : array, shape (n_components, n_components)
      Normalization matrix needed for embedding.
      Square root of the kernel matrix on ``components_``.
  
  
  References
  ----------
  * Williams, C.K.I. and Seeger, M.
    "Using the Nystroem method to speed up kernel machines",
    Advances in neural information processing systems 2001
  
  * T. Yang, Y. Li, M. Mahdavi, R. Jin and Z. Zhou
    "Nystroem Method vs Random Fourier Features: A Theoretical and Empirical
    Comparison",
    Advances in Neural Information Processing Systems 2012
  
  
  See also
  --------
  RBFSampler : An approximation to the RBF kernel using random Fourier
               features.
  
  sklearn.metrics.pairwise.kernel_metrics : List of built-in kernels.
docstr: Apply feature map to X.
  
  Computes an approximate feature map using the kernel
  between some training points and X.
  
  Parameters
  ----------
  X : array-like, shape=(n_samples, n_features)
      Data to transform.
  
  Returns
  -------
  X_transformed : array, shape=(n_samples, n_components)
      Transformed data.
docstr_tok: ["Apply","feature","map","to","X.","Computes","an","approximate","feature","map","using","the","kernel","between","some","training","points","and","X.","Parameters","--","--","--","--","--","X",":","array-like",",","shape=","(","n_samples",",","n_features",")","Data","to","transform",".","Returns","--","--","--","-","X_transformed",":","array",",","shape=","(","n_samples",",","n_components",")","Transformed","data","."]
code: def transform(self, X):
      """Apply feature map to X.
  
      Computes an approximate feature map using the kernel
      between some training points and X.
  
      Parameters
      ----------
      X : array-like, shape=(n_samples, n_features)
          Data to transform.
  
      Returns
      -------
      X_transformed : array, shape=(n_samples, n_components)
          Transformed data.
      """
      check_is_fitted(self, "components_")
      X = check_array(X, accept_sparse="csr")
  
      kernel_params = self._get_kernel_params()
      embedded = pairwise_kernels(
          X,
          self.components_,
          metric=self.kernel,
          filter_params=True,
          **kernel_params,
      )
      return np.dot(embedded, self.normalization_.T)
  
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 3 for calling the Nystroem obj's underlying member method code with example usage: obj.transform(X) and returns: np