code: def forward(
      ctx,
      shapes,
      func,
      y0,
      t,
      rtol,
      atol,
      method,
      options,
      event_fn,
      adjoint_rtol,
      adjoint_atol,
      adjoint_method,
      adjoint_options,
      t_requires_grad,
      *adjoint_params,
  ):
      ctx.shapes = shapes
      ctx.func = func
      ctx.adjoint_rtol = adjoint_rtol
      ctx.adjoint_atol = adjoint_atol
      ctx.adjoint_method = adjoint_method
      ctx.adjoint_options = adjoint_options
      ctx.t_requires_grad = t_requires_grad
      ctx.event_mode = event_fn is not None
  
      with torch.no_grad():
          ans = odeint(
              func,
              y0,
              t,
              rtol=rtol,
              atol=atol,
              method=method,
              options=options,
              event_fn=event_fn,
          )
  
          if event_fn is None:
              y = ans
              ctx.save_for_backward(t, y, *adjoint_params)
          else:
              event_t, y = ans
              ctx.save_for_backward(t, y, event_t, *adjoint_params)
  
      return ans
  
file_path: torchdiffeq/torchdiffeq/_impl/adjoint.py
imports: import warnings
  import torch
  import torch.nn
  from .odeint import SOLVERS, odeint
  from .misc import _check_inputs, _flat_to_shape, _mixed_norm, _all_callback_names, _all_adjoint_callback_names
label_desc: Use image node_id 1 for calling the OdeintAdjointMethod obj's underlying member method code with example usage: obj.forward(ctx, shapes, func, y0, t, rtol, atol, method, options, event_fn, adjoint_rtol, adjoint_atol, adjoint_method, adjoint_options, t_requires_grad) and returns: ans