code: def bicgstab(
      A, b, x0=None, *, tol=1e-5, atol=0.0, maxiter=None, M=None
  ):
      """Use Bi-Conjugate Gradient Stable iteration to solve ``Ax = b``.
  
      The numerics of JAX's ``bicgstab`` should exact match SciPy's
      ``bicgstab`` (up to numerical precision), but note that the interface
      is slightly different: you need to supply the linear operator ``A`` as
      a function instead of a sparse matrix or ``LinearOperator``.
  
      As with ``cg``, derivatives of ``bicgstab`` are implemented via implicit
      differentiation with another ``bicgstab`` solve, rather than by
      differentiating *through* the solver. They will be accurate only if
      both solves converge.
  
      Parameters
      ----------
      A: ndarray, function, or matmul-compatible object
          2D array or function that calculates the linear map (matrix-vector
          product) ``Ax`` when called like ``A(x)`` or ``A @ x``. ``A`` can represent
          any general (nonsymmetric) linear operator, and function must return array(s)
          with the same structure and shape as its argument.
      b : array or tree of arrays
          Right hand side of the linear system representing a single vector. Can be
          stored as an array or Python container of array(s) with any shape.
  
      Returns
      -------
      x : array or tree of arrays
          The converged solution. Has the same structure as ``b``.
      info : None
          Placeholder for convergence information. In the future, JAX will report
          the number of iterations when convergence is not achieved, like SciPy.
  
      Other Parameters
      ----------------
      x0 : array or tree of arrays
          Starting guess for the solution. Must have the same structure as ``b``.
      tol, atol : float, optional
          Tolerances for convergence, ``norm(residual) <= max(tol*norm(b), atol)``.
          We do not implement SciPy's "legacy" behavior, so JAX's tolerance will
          differ from SciPy unless you explicitly pass ``atol`` to SciPy's ``cg``.
      maxiter : integer
          Maximum number of iterations.  Iteration will stop after maxiter
          steps even if the specified tolerance has not been achieved.
      M : ndarray, function, or matmul-compatible object
          Preconditioner for A.  The preconditioner should approximate the
          inverse of A.  Effective preconditioning dramatically improves the
          rate of convergence, which implies that fewer iterations are needed
          to reach a given error tolerance.
  
      See also
      --------
      scipy.sparse.linalg.bicgstab
      jax.lax.custom_linear_solve
      """
  
      return _isolve(
          _bicgstab_solve,
          A=A,
          b=b,
          x0=x0,
          tol=tol,
          atol=atol,
          maxiter=maxiter,
          M=M,
      )
  
imports: from functools import partial
  import operator
  import numpy
  import jax
  import jax.numpy
  from jax import device_put
  from jax import lax
  from jax import scipy
  from jax.tree_util import tree_leaves, tree_map, tree_structure, tree_reduce, Partial
  from jax._src import dtypes
  from jax._src.lax import lax
  from jax._src.util import safe_map
label_desc: Use image node_id 26 for calling a global function with example usage: bicgstab(A, b, x0) and returns: _isolve