code: def make_system(A, M, x0, b):
      """Make a linear system Ax=b
  
      Parameters
      ----------
      A : LinearOperator
          sparse or dense matrix (or any valid input to aslinearoperator)
      M : {LinearOperator, Nones}
          preconditioner
          sparse or dense matrix (or any valid input to aslinearoperator)
      x0 : {array_like, str, None}
          initial guess to iterative method.
          ``x0 = 'Mb'`` means using the nonzero initial guess ``M @ b``.
          Default is `None`, which means using the zero initial guess.
      b : array_like
          right hand side
  
      Returns
      -------
      (A, M, x, b, postprocess)
          A : LinearOperator
              matrix of the linear system
          M : LinearOperator
              preconditioner
          x : rank 1 ndarray
              initial guess
          b : rank 1 ndarray
              right hand side
          postprocess : function
              converts the solution vector to the appropriate
              type and dimensions (e.g. (N,1) matrix)
  
      """
      A_ = A
      A = aslinearoperator(A)
  
      if A.shape[0] != A.shape[1]:
          raise ValueError(
              f"expected square matrix, but got shape={(A.shape,)}"
          )
  
      N = A.shape[0]
  
      b = asanyarray(b)
  
      if not (b.shape == (N, 1) or b.shape == (N,)):
          raise ValueError(
              f"shapes of A {A.shape} and b {b.shape} are "
              "incompatible"
          )
  
      if b.dtype.char not in "fdFD":
          b = b.astype("d")  # upcast non-FP types to double
  
      def postprocess(x):
          return x
  
      if hasattr(A, "dtype"):
          xtype = A.dtype.char
      else:
          xtype = A.matvec(b).dtype.char
      xtype = coerce(xtype, b.dtype.char)
  
      b = asarray(b, dtype=xtype)  # make b the same type as x
      b = b.ravel()
  
      # process preconditioner
      if M is None:
          if hasattr(A_, "psolve"):
              psolve = A_.psolve
          else:
              psolve = id
          if hasattr(A_, "rpsolve"):
              rpsolve = A_.rpsolve
          else:
              rpsolve = id
          if psolve is id and rpsolve is id:
              M = IdentityOperator(shape=A.shape, dtype=A.dtype)
          else:
              M = LinearOperator(
                  A.shape, matvec=psolve, rmatvec=rpsolve, dtype=A.dtype
              )
      else:
          M = aslinearoperator(M)
          if A.shape != M.shape:
              raise ValueError(
                  "matrix and preconditioner have different shapes"
              )
  
      # set initial guess
      if x0 is None:
          x = zeros(N, dtype=xtype)
      elif isinstance(x0, str):
          if x0 == "Mb":  # use nonzero initial guess ``M @ b``
              bCopy = b.copy()
              x = M.matvec(bCopy)
      else:
          x = array(x0, dtype=xtype)
          if not (x.shape == (N, 1) or x.shape == (N,)):
              raise ValueError(
                  f"shapes of A {A.shape} and "
                  f"x0 {x.shape} are incompatible"
              )
          x = x.ravel()
  
      return A, M, x, b, postprocess
  
imports: from numpy import asanyarray, asarray, array, zeros
  from scipy.sparse.linalg._interface import aslinearoperator, LinearOperator, IdentityOperator
label_desc: Use image node_id 3 for calling a global function with example usage: make_system(A, M, x0, b) and returns: A,  M,  x,  b,  postprocess, x