class_docstr: Test the speed of four least-squares solvers on not full rank matrices.
  Also check the difference in the solutions.
  
  The matrix has the size ``(m, 2/3*m)``; the rank is ``1/2 * m``.
  Matrix values are random in the range (-5, 5), the same is for the right
  hand side.  The complex matrix is the sum of real and imaginary matrices.
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code: def setup(self, dtype, size, lapack_driver):
      if lapack_driver == "numpy" and size >= 200:
          # skip: slow, and not useful to benchmark numpy
          raise NotImplementedError()
  
      rng = np.random.default_rng(1234)
      n = math.ceil(2.0 / 3.0 * size)
      k = math.ceil(1.0 / 2.0 * size)
      m = size
  
      if dtype is np.complex128:
          A = (10 * rng.random((m, k)) - 5) + 1j * (
              10 * rng.random((m, k)) - 5
          )
          temp = (10 * rng.random((k, n)) - 5) + 1j * (
              10 * rng.random((k, n)) - 5
          )
          b = (10 * rng.random((m, 1)) - 5) + 1j * (
              10 * rng.random((m, 1)) - 5
          )
      else:
          A = 10 * rng.random((m, k)) - 5
          temp = 10 * rng.random((k, n)) - 5
          b = 10 * rng.random((m, 1)) - 5
  
      self.A = A.dot(temp)
      self.b = b
  
imports: import math
  import numpy.linalg
  import numpy
  from numpy.testing import assert_
  from numpy.random import rand
  from .common import Benchmark, safe_import
label_desc: Use image node_id 1 for calling the Lstsq obj's underlying member method code with example usage: obj.setup(dtype, size, lapack_driver) without return types