code: def svd_compressed(
      a,
      k,
      iterator="power",
      n_power_iter=0,
      n_oversamples=10,
      seed=None,
      compute=False,
      coerce_signs=True,
  ):
      """Randomly compressed rank-k thin Singular Value Decomposition.
  
      This computes the approximate singular value decomposition of a large
      array.  This algorithm is generally faster than the normal algorithm
      but does not provide exact results.  One can balance between
      performance and accuracy with input parameters (see below).
  
      Parameters
      ----------
      a: Array
          Input array
      k: int
          Rank of the desired thin SVD decomposition.
      iterator: {'power', 'QR'}, default='power'
          Define the technique used for iterations to cope with flat
          singular spectra or when the input matrix is very large.
      n_power_iter: int, default=0
          Number of power iterations, useful when the singular values
          decay slowly. Error decreases exponentially as `n_power_iter`
          increases. In practice, set `n_power_iter` <= 4.
      n_oversamples: int, default=10
          Number of oversamples used for generating the sampling matrix.
          This value increases the size of the subspace computed, which is more
          accurate at the cost of efficiency.  Results are rarely sensitive to this choice
          though and in practice a value of 10 is very commonly high enough.
      compute : bool
          Whether or not to compute data at each use.
          Recomputing the input while performing several passes reduces memory
          pressure, but means that we have to compute the input multiple times.
          This is a good choice if the data is larger than memory and cheap to
          recreate.
      coerce_signs : bool
          Whether or not to apply sign coercion to singular vectors in
          order to maintain deterministic results, by default True.
  
  
      Examples
      --------
      >>> u, s, v = svd_compressed(x, 20)  # doctest: +SKIP
  
      Returns
      -------
      u:  Array, unitary / orthogonal
      s:  Array, singular values in decreasing order (largest first)
      v:  Array, unitary / orthogonal
  
      References
      ----------
      N. Halko, P. G. Martinsson, and J. A. Tropp.
      Finding structure with randomness: Probabilistic algorithms for
      constructing approximate matrix decompositions.
      SIAM Rev., Survey and Review section, Vol. 53, num. 2,
      pp. 217-288, June 2011
      https://arxiv.org/abs/0909.4061
      """
      comp = compression_matrix(
          a,
          k,
          iterator=iterator,
          n_power_iter=n_power_iter,
          n_oversamples=n_oversamples,
          seed=seed,
          compute=compute,
      )
      if compute:
          comp = comp.persist()
          wait(comp)
      a_compressed = comp.dot(a)
      v, s, u = tsqr(a_compressed.T, compute_svd=True)
      u = comp.T.dot(u.T)
      v = v.T
      u = u[:, :k]
      s = s[:k]
      v = v[:k, :]
      if coerce_signs:
          u, v = svd_flip(u, v)
      return u, s, v
  
imports: from __future__ import annotations
  import operator
  import warnings
  from functools import partial
  from numbers import Number
  import numpy
  import tlz
  from dask.array.core import Array, concatenate, dotmany, from_delayed
  from dask.array.creation import eye
  from dask.array.random import RandomState, default_rng
  from dask.array.utils import array_safe, meta_from_array, solve_triangular_safe, svd_flip
  from dask.base import tokenize, wait
  from dask.blockwise import blockwise
  from dask.delayed import delayed
  from dask.highlevelgraph import HighLevelGraph
  from dask.utils import apply, derived_from
label_desc: Use image node_id 9 for calling a global function with example usage: svd_compressed(a, k, iterator, n_power_iter, n_oversamples, seed, compute, coerce_signs) and returns: u,  s,  v