code: def _interpolate_solution_at(
      target_time, solver_state, validate_args=False
  ):
      """Computes the solution at `target_time` using 4th order interpolation.
  
      Args:
        target_time: Floating `Tensor` specifying the time at which to obtain the
          solution. Must be within the interval of the last time step of the
          `solver_state`: `solver_state.last_step_start` <= `target_time` <=
          `solver_state.current_time`.
        solver_state: `_DopriSolverInternalState` - solver state.
        validate_args: Python `bool` indicating whether to validate inputs.
          Default value: False.
  
      Returns:
        solution: Solution at `target_time` obtained by interpolation.
        coefficients: Interpolating coefficients used to construct the solution.
      """
      coefficients = solver_state.interpolating_coefficients
      t0 = solver_state.last_step_start
      t1 = solver_state.current_time
      solution = rk_util.evaluate_interpolation(
          coefficients, t0, t1, target_time, validate_args
      )
      return solution, coefficients
  
imports: import collections
  import functools
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.math.ode import base
  from tensorflow_probability.python.math.ode import runge_kutta_util
  from tensorflow_probability.python.math.ode import util
  from tensorflow.python.util import nest
label_desc: Use image node_id 3 for calling a global function with example usage: _interpolate_solution_at(target_time, solver_state, validate_args) and returns: solution,  coefficients