code: def solve(
      a,
      b,
      sym_pos=False,
      lower=False,
      overwrite_a=False,
      overwrite_b=False,
      debug=None,
      check_finite=True,
      assume_a="gen",
      transposed=False,
  ):
      """
      Solves the linear equation set ``a * x = b`` for the unknown ``x``
      for square ``a`` matrix.
  
      If the data matrix is known to be a particular type then supplying the
      corresponding string to ``assume_a`` key chooses the dedicated solver.
      The available options are
  
      ===================  ========
       generic matrix       'gen'
       symmetric            'sym'
       hermitian            'her'
       positive definite    'pos'
      ===================  ========
  
      If omitted, ``'gen'`` is the default structure.
  
      The datatype of the arrays define which solver is called regardless
      of the values. In other words, even when the complex array entries have
      precisely zero imaginary parts, the complex solver will be called based
      on the data type of the array.
  
      Parameters
      ----------
      a : (N, N) array_like
          Square input data
      b : (N, NRHS) array_like
          Input data for the right hand side.
      sym_pos : bool, optional
          Assume `a` is symmetric and positive definite. This key is deprecated
          and assume_a = 'pos' keyword is recommended instead. The functionality
          is the same. It will be removed in the future.
      lower : bool, optional
          If True, only the data contained in the lower triangle of `a`. Default
          is to use upper triangle. (ignored for ``'gen'``)
      overwrite_a : bool, optional
          Allow overwriting data in `a` (may enhance performance).
          Default is False.
      overwrite_b : bool, optional
          Allow overwriting data in `b` (may enhance performance).
          Default is False.
      check_finite : bool, optional
          Whether to check that the input matrices contain only finite numbers.
          Disabling may give a performance gain, but may result in problems
          (crashes, non-termination) if the inputs do contain infinities or NaNs.
      assume_a : str, optional
          Valid entries are explained above.
      transposed: bool, optional
          If True, ``a^T x = b`` for real matrices, raises `NotImplementedError`
          for complex matrices (only for True).
  
      Returns
      -------
      x : (N, NRHS) ndarray
          The solution array.
  
      Raises
      ------
      ValueError
          If size mismatches detected or input a is not square.
      LinAlgError
          If the matrix is singular.
      LinAlgWarning
          If an ill-conditioned input a is detected.
      NotImplementedError
          If transposed is True and input a is a complex matrix.
  
      Examples
      --------
      Given `a` and `b`, solve for `x`:
  
      >>> a = np.array([[3, 2, 0], [1, -1, 0], [0, 5, 1]])
      >>> b = np.array([2, 4, -1])
      >>> from scipy import linalg
      >>> x = linalg.solve(a, b)
      >>> x
      array([ 2., -2.,  9.])
      >>> np.dot(a, x) == b
      array([ True,  True,  True], dtype=bool)
  
      Notes
      -----
      If the input b matrix is a 1D array with N elements, when supplied
      together with an NxN input a, it is assumed as a valid column vector
      despite the apparent size mismatch. This is compatible with the
      numpy.dot() behavior and the returned result is still 1D array.
  
      The generic, symmetric, hermitian and positive definite solutions are
      obtained via calling ?GESV, ?SYSV, ?HESV, and ?POSV routines of
      LAPACK respectively.
      """
      # Flags for 1D or nD right hand side
      b_is_1D = False
  
      a1 = atleast_2d(_asarray_validated(a, check_finite=check_finite))
      b1 = atleast_1d(_asarray_validated(b, check_finite=check_finite))
      n = a1.shape[0]
  
      overwrite_a = overwrite_a or _datacopied(a1, a)
      overwrite_b = overwrite_b or _datacopied(b1, b)
  
      if a1.shape[0] != a1.shape[1]:
          raise ValueError("Input a needs to be a square matrix.")
  
      if n != b1.shape[0]:
          # Last chance to catch 1x1 scalar a and 1D b arrays
          if not (n == 1 and b1.size != 0):
              raise ValueError(
                  "Input b has to have same number of rows as "
                  "input a"
              )
  
      # accommodate empty arrays
      if b1.size == 0:
          return np.asfortranarray(b1.copy())
  
      # regularize 1D b arrays to 2D
      if b1.ndim == 1:
          if n == 1:
              b1 = b1[None, :]
          else:
              b1 = b1[:, None]
          b_is_1D = True
  
      # Backwards compatibility - old keyword.
      if sym_pos:
          assume_a = "pos"
  
      if assume_a not in ("gen", "sym", "her", "pos"):
          raise ValueError(
              "{} is not a recognized matrix structure"
              "".format(assume_a)
          )
  
      # Deprecate keyword "debug"
      if debug is not None:
          warn(
              'Use of the "debug" keyword is deprecated '
              "and this keyword will be removed in future "
              "versions of SciPy.",
              DeprecationWarning,
              stacklevel=2,
          )
  
      # Get the correct lamch function.
      # The LAMCH functions only exists for S and D
      # So for complex values we have to convert to real/double.
      if a1.dtype.char in "fF":  # single precision
          lamch = get_lapack_funcs("lamch", dtype="f")
      else:
          lamch = get_lapack_funcs("lamch", dtype="d")
  
      # Currently we do not have the other forms of the norm calculators
      #   lansy, lanpo, lanhe.
      # However, in any case they only reduce computations slightly...
      lange = get_lapack_funcs("lange", (a1,))
  
      # Since the I-norm and 1-norm are the same for symmetric matrices
      # we can collect them all in this one call
      # Note however, that when issuing 'gen' and form!='none', then
      # the I-norm should be used
      if transposed:
          trans = 1
          norm = "I"
          if np.iscomplexobj(a1):
              raise NotImplementedError(
                  "scipy.linalg.solve can currently "
                  "not solve a^T x = b or a^H x = b "
                  "for complex matrices."
              )
      else:
          trans = 0
          norm = "1"
  
      anorm = lange(norm, a1)
  
      # Generalized case 'gesv'
      if assume_a == "gen":
          gecon, getrf, getrs = get_lapack_funcs(
              ("gecon", "getrf", "getrs"), (a1, b1)
          )
          lu, ipvt, info = getrf(a1, overwrite_a=overwrite_a)
          _solve_check(n, info)
          x, info = getrs(
              lu, ipvt, b1, trans=trans, overwrite_b=overwrite_b
          )
          _solve_check(n, info)
          rcond, info = gecon(lu, anorm, norm=norm)
      # Hermitian case 'hesv'
      elif assume_a == "her":
          hecon, hesv, hesv_lw = get_lapack_funcs(
              ("hecon", "hesv", "hesv_lwork"), (a1, b1)
          )
          lwork = _compute_lwork(hesv_lw, n, lower)
          lu, ipvt, x, info = hesv(
              a1,
              b1,
              lwork=lwork,
              lower=lower,
              overwrite_a=overwrite_a,
              overwrite_b=overwrite_b,
          )
          _solve_check(n, info)
          rcond, info = hecon(lu, ipvt, anorm)
      # Symmetric case 'sysv'
      elif assume_a == "sym":
          sycon, sysv, sysv_lw = get_lapack_funcs(
              ("sycon", "sysv", "sysv_lwork"), (a1, b1)
          )
          lwork = _compute_lwork(sysv_lw, n, lower)
          lu, ipvt, x, info = sysv(
              a1,
              b1,
              lwork=lwork,
              lower=lower,
              overwrite_a=overwrite_a,
              overwrite_b=overwrite_b,
          )
          _solve_check(n, info)
          rcond, info = sycon(lu, ipvt, anorm)
      # Positive definite case 'posv'
      else:
          pocon, posv = get_lapack_funcs(("pocon", "posv"), (a1, b1))
          lu, x, info = posv(
              a1,
              b1,
              lower=lower,
              overwrite_a=overwrite_a,
              overwrite_b=overwrite_b,
          )
          _solve_check(n, info)
          rcond, info = pocon(lu, anorm)
  
      _solve_check(n, info, lamch, rcond)
  
      if b_is_1D:
          x = x.ravel()
  
      return x
  
imports: from __future__ import division, print_function, absolute_import
  from warnings import warn
  import numpy
  from numpy import atleast_1d, atleast_2d
  from .flinalg import get_flinalg_funcs
  from .lapack import get_lapack_funcs, _compute_lwork
  from .misc import LinAlgError, _datacopied, LinAlgWarning
  from .decomp import _asarray_validated
  from .None import decomp, decomp_svd
  from ._solve_toeplitz import levinson
label_desc: Use image node_id 2 for calling a global function with example usage: solve(a, b, sym_pos, lower, overwrite_a, overwrite_b, debug, check_finite, assume_a, transposed) and returns: x, np