code: def randomized_svd(
      M,
      n_components,
      *,
      n_oversamples=10,
      n_iter="auto",
      power_iteration_normalizer="auto",
      transpose="auto",
      flip_sign=True,
      random_state=None,
      svd_lapack_driver="gesdd",
  ):
      """Compute a truncated randomized SVD.
  
      This method solves the fixed-rank approximation problem described in [1]_
      (problem (1.5), p5).
  
      Parameters
      ----------
      M : {ndarray, sparse matrix}
          Matrix to decompose.
  
      n_components : int
          Number of singular values and vectors to extract.
  
      n_oversamples : int, default=10
          Additional number of random vectors to sample the range of M so as
          to ensure proper conditioning. The total number of random vectors
          used to find the range of M is n_components + n_oversamples. Smaller
          number can improve speed but can negatively impact the quality of
          approximation of singular vectors and singular values. Users might wish
          to increase this parameter up to `2*k - n_components` where k is the
          effective rank, for large matrices, noisy problems, matrices with
          slowly decaying spectrums, or to increase precision accuracy. See [1]_
          (pages 5, 23 and 26).
  
      n_iter : int or 'auto', default='auto'
          Number of power iterations. It can be used to deal with very noisy
          problems. When 'auto', it is set to 4, unless `n_components` is small
          (< .1 * min(X.shape)) in which case `n_iter` is set to 7.
          This improves precision with few components. Note that in general
          users should rather increase `n_oversamples` before increasing `n_iter`
          as the principle of the randomized method is to avoid usage of these
          more costly power iterations steps. When `n_components` is equal
          or greater to the effective matrix rank and the spectrum does not
          present a slow decay, `n_iter=0` or `1` should even work fine in theory
          (see [1]_ page 9).
  
          .. versionchanged:: 0.18
  
      power_iteration_normalizer : {'auto', 'QR', 'LU', 'none'}, default='auto'
          Whether the power iterations are normalized with step-by-step
          QR factorization (the slowest but most accurate), 'none'
          (the fastest but numerically unstable when `n_iter` is large, e.g.
          typically 5 or larger), or 'LU' factorization (numerically stable
          but can lose slightly in accuracy). The 'auto' mode applies no
          normalization if `n_iter` <= 2 and switches to LU otherwise.
  
          .. versionadded:: 0.18
  
      transpose : bool or 'auto', default='auto'
          Whether the algorithm should be applied to M.T instead of M. The
          result should approximately be the same. The 'auto' mode will
          trigger the transposition if M.shape[1] > M.shape[0] since this
          implementation of randomized SVD tend to be a little faster in that
          case.
  
          .. versionchanged:: 0.18
  
      flip_sign : bool, default=True
          The output of a singular value decomposition is only unique up to a
          permutation of the signs of the singular vectors. If `flip_sign` is
          set to `True`, the sign ambiguity is resolved by making the largest
          loadings for each component in the left singular vectors positive.
  
      random_state : int, RandomState instance or None, default='warn'
          The seed of the pseudo random number generator to use when
          shuffling the data, i.e. getting the random vectors to initialize
          the algorithm. Pass an int for reproducible results across multiple
          function calls. See :term:`Glossary <random_state>`.
  
          .. versionchanged:: 1.2
              The default value changed from 0 to None.
  
      svd_lapack_driver : {"gesdd", "gesvd"}, default="gesdd"
          Whether to use the more efficient divide-and-conquer approach
          (`"gesdd"`) or more general rectangular approach (`"gesvd"`) to compute
          the SVD of the matrix B, which is the projection of M into a low
          dimensional subspace, as described in [1]_.
  
          .. versionadded:: 1.2
  
      Returns
      -------
      u : ndarray of shape (n_samples, n_components)
          Unitary matrix having left singular vectors with signs flipped as columns.
      s : ndarray of shape (n_components,)
          The singular values, sorted in non-increasing order.
      vh : ndarray of shape (n_components, n_features)
          Unitary matrix having right singular vectors with signs flipped as rows.
  
      Notes
      -----
      This algorithm finds a (usually very good) approximate truncated
      singular value decomposition using randomization to speed up the
      computations. It is particularly fast on large matrices on which
      you wish to extract only a small number of components. In order to
      obtain further speed up, `n_iter` can be set <=2 (at the cost of
      loss of precision). To increase the precision it is recommended to
      increase `n_oversamples`, up to `2*k-n_components` where k is the
      effective rank. Usually, `n_components` is chosen to be greater than k
      so increasing `n_oversamples` up to `n_components` should be enough.
  
      References
      ----------
      .. [1] :arxiv:`"Finding structure with randomness:
        Stochastic algorithms for constructing approximate matrix decompositions"
        <0909.4061>`
        Halko, et al. (2009)
  
      .. [2] A randomized algorithm for the decomposition of matrices
        Per-Gunnar Martinsson, Vladimir Rokhlin and Mark Tygert
  
      .. [3] An implementation of a randomized algorithm for principal component
        analysis A. Szlam et al. 2014
  
      Examples
      --------
      >>> import numpy as np
      >>> from sklearn.utils.extmath import randomized_svd
      >>> a = np.array([[1, 2, 3, 5],
      ...               [3, 4, 5, 6],
      ...               [7, 8, 9, 10]])
      >>> U, s, Vh = randomized_svd(a, n_components=2, random_state=0)
      >>> U.shape, s.shape, Vh.shape
      ((3, 2), (2,), (2, 4))
      """
      if sparse.issparse(M) and M.format in ("lil", "dok"):
          warnings.warn(
              "Calculating SVD of a {} is expensive. "
              "csr_matrix is more efficient.".format(type(M).__name__),
              sparse.SparseEfficiencyWarning,
          )
  
      random_state = check_random_state(random_state)
      n_random = n_components + n_oversamples
      n_samples, n_features = M.shape
  
      if n_iter == "auto":
          # Checks if the number of iterations is explicitly specified
          # Adjust n_iter. 7 was found a good compromise for PCA. See #5299
          n_iter = 7 if n_components < 0.1 * min(M.shape) else 4
  
      if transpose == "auto":
          transpose = n_samples < n_features
      if transpose:
          # this implementation is a bit faster with smaller shape[1]
          M = M.T
  
      Q = randomized_range_finder(
          M,
          size=n_random,
          n_iter=n_iter,
          power_iteration_normalizer=power_iteration_normalizer,
          random_state=random_state,
      )
  
      # project M to the (k + p) dimensional space using the basis vectors
      B = safe_sparse_dot(Q.T, M)
  
      # compute the SVD on the thin matrix: (k + p) wide
      Uhat, s, Vt = linalg.svd(
          B, full_matrices=False, lapack_driver=svd_lapack_driver
      )
  
      del B
      U = np.dot(Q, Uhat)
  
      if flip_sign:
          if not transpose:
              U, Vt = svd_flip(U, Vt)
          else:
              # In case of transpose u_based_decision=false
              # to actually flip based on u and not v.
              U, Vt = svd_flip(U, Vt, u_based_decision=False)
  
      if transpose:
          # transpose back the results according to the input convention
          return (
              Vt[:n_components, :].T,
              s[:n_components],
              U[:, :n_components].T,
          )
      else:
          return (
              U[:, :n_components],
              s[:n_components],
              Vt[:n_components, :],
          )
  
imports: import warnings
  import numpy
  from scipy import linalg, sparse
  from .None import check_random_state
  from ._array_api import _is_numpy_namespace, get_namespace
  from ._logistic_sigmoid import _log_logistic_sigmoid
  from .sparsefuncs_fast import csr_row_norms
  from .validation import check_array
label_desc: Use image node_id 7 for calling a global function with example usage: randomized_svd(M, n_components) and returns: Vt,  s,  U, U,  s,  Vt