code: def clarkson_woodruff_transform(input_matrix, sketch_size, seed=None):
      r"""
      Applies a Clarkson-Woodruff Transform/sketch to the input matrix.
  
      Given an input_matrix ``A`` of size ``(n, d)``, compute a matrix ``A'`` of
      size (sketch_size, d) so that
  
      .. math:: \|Ax\| \approx \|A'x\|
  
      with high probability via the Clarkson-Woodruff Transform, otherwise
      known as the CountSketch matrix.
  
      Parameters
      ----------
      input_matrix: array_like
          Input matrix, of shape ``(n, d)``.
      sketch_size: int
          Number of rows for the sketch.
      seed : {None, int, `numpy.random.Generator`,
              `numpy.random.RandomState`}, optional
  
          If `seed` is None (or `np.random`), the `numpy.random.RandomState`
          singleton is used.
          If `seed` is an int, a new ``RandomState`` instance is used,
          seeded with `seed`.
          If `seed` is already a ``Generator`` or ``RandomState`` instance then
          that instance is used.
  
      Returns
      -------
      A' : array_like
          Sketch of the input matrix ``A``, of size ``(sketch_size, d)``.
  
      Notes
      -----
      To make the statement
  
      .. math:: \|Ax\| \approx \|A'x\|
  
      precise, observe the following result which is adapted from the
      proof of Theorem 14 of [2]_ via Markov's Inequality. If we have
      a sketch size ``sketch_size=k`` which is at least
  
      .. math:: k \geq \frac{2}{\epsilon^2\delta}
  
      Then for any fixed vector ``x``,
  
      .. math:: \|Ax\| = (1\pm\epsilon)\|A'x\|
  
      with probability at least one minus delta.
  
      This implementation takes advantage of sparsity: computing
      a sketch takes time proportional to ``A.nnz``. Data ``A`` which
      is in ``scipy.sparse.csc_matrix`` format gives the quickest
      computation time for sparse input.
  
      >>> from scipy import linalg
      >>> from scipy import sparse
      >>> rng = np.random.default_rng()
      >>> n_rows, n_columns, density, sketch_n_rows = 15000, 100, 0.01, 200
      >>> A = sparse.rand(n_rows, n_columns, density=density, format='csc')
      >>> B = sparse.rand(n_rows, n_columns, density=density, format='csr')
      >>> C = sparse.rand(n_rows, n_columns, density=density, format='coo')
      >>> D = rng.standard_normal((n_rows, n_columns))
      >>> SA = linalg.clarkson_woodruff_transform(A, sketch_n_rows) # fastest
      >>> SB = linalg.clarkson_woodruff_transform(B, sketch_n_rows) # fast
      >>> SC = linalg.clarkson_woodruff_transform(C, sketch_n_rows) # slower
      >>> SD = linalg.clarkson_woodruff_transform(D, sketch_n_rows) # slowest
  
      That said, this method does perform well on dense inputs, just slower
      on a relative scale.
  
      Examples
      --------
      Given a big dense matrix ``A``:
  
      >>> from scipy import linalg
      >>> n_rows, n_columns, sketch_n_rows = 15000, 100, 200
      >>> rng = np.random.default_rng()
      >>> A = rng.standard_normal((n_rows, n_columns))
      >>> sketch = linalg.clarkson_woodruff_transform(A, sketch_n_rows)
      >>> sketch.shape
      (200, 100)
      >>> norm_A = np.linalg.norm(A)
      >>> norm_sketch = np.linalg.norm(sketch)
  
      Now with high probability, the true norm ``norm_A`` is close to
      the sketched norm ``norm_sketch`` in absolute value.
  
      Similarly, applying our sketch preserves the solution to a linear
      regression of :math:`\min \|Ax - b\|`.
  
      >>> from scipy import linalg
      >>> n_rows, n_columns, sketch_n_rows = 15000, 100, 200
      >>> rng = np.random.default_rng()
      >>> A = rng.standard_normal((n_rows, n_columns))
      >>> b = rng.standard_normal(n_rows)
      >>> x = np.linalg.lstsq(A, b, rcond=None)
      >>> Ab = np.hstack((A, b.reshape(-1,1)))
      >>> SAb = linalg.clarkson_woodruff_transform(Ab, sketch_n_rows)
      >>> SA, Sb = SAb[:,:-1], SAb[:,-1]
      >>> x_sketched = np.linalg.lstsq(SA, Sb, rcond=None)
  
      As with the matrix norm example, ``np.linalg.norm(A @ x - b)``
      is close to ``np.linalg.norm(A @ x_sketched - b)`` with high
      probability.
  
      References
      ----------
      .. [1] Kenneth L. Clarkson and David P. Woodruff. Low rank approximation and
             regression in input sparsity time. In STOC, 2013.
  
      .. [2] David P. Woodruff. Sketching as a tool for numerical linear algebra.
             In Foundations and Trends in Theoretical Computer Science, 2014.
  
      """
      S = cwt_matrix(sketch_size, input_matrix.shape[0], seed)
      return S.dot(input_matrix)
  
imports: import numpy
  from scipy._lib._util import check_random_state, rng_integers
  from scipy.sparse import csc_matrix
label_desc: Use image node_id 2 for calling a global function with example usage: clarkson_woodruff_transform(input_matrix, sketch_size, seed) and returns: S