code: def solve(a, b):
      r"""
      Solve a linear matrix equation, or system of linear scalar equations.
  
      Computes the "exact" solution, `x`, of the well-determined, i.e., full
      rank, linear matrix equation `ax = b`.
  
      Parameters
      ----------
      a : (..., M, M) ndarray
          Coefficient matrix.
      b : {(..., M,), (..., M, K)}, ndarray
          Ordinate or "dependent variable" values.
  
      Returns
      -------
      x : {(..., M,), (..., M, K)} ndarray
          Solution to the system a x = b.  Returned shape is identical to `b`.
  
      Raises
      ------
      MXNetError
          If `a` is singular or not square.
  
      Notes
      -----
      Broadcasting rules apply, see the `numpy.linalg` documentation for
      details.
  
      The solutions are computed using LAPACK routine ``_gesv``.
  
      `a` must be square and of full-rank, i.e., all rows (or, equivalently,
      columns) must be linearly independent; if either is not true, use
      `lstsq` for the least-squares best "solution" of the
      system/equation.
  
      Examples
      --------
      Solve the system of equations ``3 * x0 + x1 = 9`` and ``x0 + 2 * x1 = 8``:
  
      >>> a = np.array([[3,1], [1,2]])
      >>> b = np.array([9,8])
      >>> x = np.linalg.solve(a, b)
      >>> x
      array([2.,  3.])
  
      Check that the solution is correct:
  
      >>> np.allclose(np.dot(a, x), b)
      True
      """
      return _api_internal.solve(a, b)
  
file_path: mxnet/python/mxnet/ndarray/numpy/linalg.py
imports: import numpy
  from .None import _op
  from .None import _api_internal
label_desc: Use image node_id 11 for calling a global function with example usage: solve(a, b) and returns: _api_internal