code: def odeint(
      func,
      y0,
      t,
      *args,
      rtol=1.4e-8,
      atol=1.4e-8,
      mxstep=jnp.inf,
      hmax=jnp.inf,
  ):
      """Adaptive stepsize (Dormand-Prince) Runge-Kutta odeint implementation.
  
      Args:
        func: function to evaluate the time derivative of the solution `y` at time
          `t` as `func(y, t, *args)`, producing the same shape/structure as `y0`.
        y0: array or pytree of arrays representing the initial value for the state.
        t: array of float times for evaluation, like `jnp.linspace(0., 10., 101)`,
          in which the values must be strictly increasing.
        *args: tuple of additional arguments for `func`, which must be arrays
          scalars, or (nested) standard Python containers (tuples, lists, dicts,
          namedtuples, i.e. pytrees) of those types.
        rtol: float, relative local error tolerance for solver (optional).
        atol: float, absolute local error tolerance for solver (optional).
        mxstep: int, maximum number of steps to take for each timepoint (optional).
        hmax: float, maximum step size allowed (optional).
  
      Returns:
        Values of the solution `y` (i.e. integrated system values) at each time
        point in `t`, represented as an array (or pytree of arrays) with the same
        shape/structure as `y0` except with a new leading axis of length `len(t)`.
      """
      for arg in tree_leaves(args):
          if not isinstance(
              arg, core.Tracer
          ) and not core.valid_jaxtype(arg):
              raise TypeError(
                  f"The contents of odeint *args must be arrays or scalars, but got {arg}."
              )
      if not jnp.issubdtype(t.dtype, jnp.floating):
          raise TypeError(f"t must be an array of floats, but got {t}.")
  
      converted, consts = custom_derivatives.closure_convert(
          func, y0, t[0], *args
      )
      return _odeint_wrapper(
          converted, rtol, atol, mxstep, hmax, y0, t, *args, *consts
      )
  
imports: from functools import partial
  import operator
  import jax
  import jax.numpy
  from jax._src import core
  from jax import custom_derivatives
  from jax import lax
  from jax._src.numpy.util import promote_dtypes_inexact
  from jax._src.util import safe_map, safe_zip
  from jax.flatten_util import ravel_pytree
  from jax.tree_util import tree_leaves, tree_map
  from jax._src import linear_util
label_desc: Use image node_id 10 for calling a global function with example usage: odeint(func, y0, t) and returns: _odeint_wrapper