code: def svd(a, coerce_signs=True):
      """
      Compute the singular value decomposition of a matrix.
  
      Parameters
      ----------
      a : (M, N) Array
      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 = da.linalg.svd(x)  # doctest: +SKIP
  
      Returns
      -------
  
      u : (M, K) Array, unitary / orthogonal
          Left-singular vectors of `a` (in columns) with shape (M, K)
          where K = min(M, N).
      s : (K,) Array, singular values in decreasing order (largest first)
          Singular values of `a`.
      v : (K, N) Array, unitary / orthogonal
          Right-singular vectors of `a` (in rows) with shape (K, N)
          where K = min(M, N).
  
      Warnings
      --------
  
      SVD is only supported for arrays with chunking in one dimension.
      This requires that all inputs either contain a single column
      of chunks (tall-and-skinny) or a single row of chunks (short-and-fat).
      For arrays with chunking in both dimensions, see da.linalg.svd_compressed.
  
      See Also
      --------
  
      np.linalg.svd : Equivalent NumPy Operation
      da.linalg.svd_compressed : Randomized SVD for fully chunked arrays
      dask.array.linalg.tsqr : QR factorization for tall-and-skinny arrays
      dask.array.utils.svd_flip : Sign normalization for singular vectors
      """
      nb = a.numblocks
      if a.ndim != 2:
          raise ValueError(
              "Array must be 2D.\n"
              "Input shape: {}\n"
              "Input ndim: {}\n".format(a.shape, a.ndim)
          )
      if nb[0] > 1 and nb[1] > 1:
          raise NotImplementedError(
              "Array must be chunked in one dimension only. "
              "This function (svd) only supports tall-and-skinny or short-and-fat "
              "matrices (see da.linalg.svd_compressed for SVD on fully chunked arrays).\n"
              "Input shape: {}\n"
              "Input numblocks: {}\n".format(a.shape, nb)
          )
  
      # Single-chunk case
      if nb[0] == nb[1] == 1:
          m, n = a.shape
          k = min(a.shape)
          mu, ms, mv = np.linalg.svd(
              np.ones_like(a._meta, shape=(1, 1), dtype=a._meta.dtype)
          )
          u, s, v = delayed(np.linalg.svd, nout=3)(
              a, full_matrices=False
          )
          u = from_delayed(u, shape=(m, k), meta=mu)
          s = from_delayed(s, shape=(k,), meta=ms)
          v = from_delayed(v, shape=(k, n), meta=mv)
      # Multi-chunk cases
      else:
          # Tall-and-skinny case
          if nb[0] > nb[1]:
              u, s, v = tsqr(a, compute_svd=True)
              truncate = a.shape[0] < a.shape[1]
          # Short-and-fat case
          else:
              vt, s, ut = tsqr(a.T, compute_svd=True)
              u, s, v = ut.T, s, vt.T
              truncate = a.shape[0] > a.shape[1]
          # Only when necessary, remove extra singular vectors if array
          # has shape that contradicts chunking, e.g. the array is a
          # column of chunks but still has more columns than rows overall
          if truncate:
              k = min(a.shape)
              u, v = u[:, :k], 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 11 for calling a global function with example usage: svd(a, coerce_signs) and returns: u,  s,  v