class_docstr: Adjoint solver which propagates step size between solves.
class_docstr_tok: ["Adjoint","solver","which","propagates","step","size","between","solves","."]
code: def _solve(self, **kwargs):
      step_size, solve_count = kwargs.pop(
          "previous_solver_internal_state"
      )
      results = self._make_solver_fn(step_size).solve(**kwargs)
      return results._replace(
          solver_internal_state=(
              results.solver_internal_state.step_size,
              solve_count + 1,
          )
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math.ode import base
  from tensorflow_probability.python.math.ode import bdf
  from tensorflow_probability.python.math.ode import dormand_prince
label_desc: Use image node_id 2 for calling the StepSizeHeuristicAdjointSolver obj's underlying member method code with example usage: obj._solve() and returns: results