docstr: Transform `x` into the Nystroem approximation.
  
  Parameters
  ----------
  x
      `torch.Tensor` of shape ``(n, d)`` where ``n`` is the number of samples and ``d`` is the dimensionality of
      the data.
  
  Returns
  -------
  `torch.Tensor` of shape ``(n, n_components)`` where ``n_components`` is the number of components in the
  Nystroem approximation.
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code: def transform(self, x: torch.Tensor) -> torch.Tensor:
      """Transform `x` into the Nystroem approximation.
  
      Parameters
      ----------
      x
          `torch.Tensor` of shape ``(n, d)`` where ``n`` is the number of samples and ``d`` is the dimensionality of
          the data.
  
      Returns
      -------
      `torch.Tensor` of shape ``(n, n_components)`` where ``n_components`` is the number of components in the
      Nystroem approximation.
      """
  
      K_xz = self.kernel(x, self.z)
      return K_xz @ self.K_zz_root_inv
  
imports: import warnings
  from typing import Callable, Dict, Optional, Tuple
  import numpy
  import torch
  from sklearn.linear_model import SGDOneClassSVM
  from sklearn.utils.extmath import safe_sparse_dot
  from tqdm import tqdm
  from typing_extensions import Self
  from alibi_detect.od.pytorch.base import TorchOutlierDetector
  from alibi_detect.utils.pytorch.losses import hinge_loss
  from alibi_detect.utils.pytorch.kernels import GaussianRBF
  from alibi_detect.utils._types import TorchDeviceType
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: unknown