code: def dg_ga_jvp_column_sum_v2(self, t, y, a):
      # Faster, but more memory intensive.
      requires_grad = torch.is_grad_enabled()
      with torch.enable_grad():
          y = y if y.requires_grad else y.detach().requires_grad_(True)
          g = self.g(t, y)
          ga = torch.bmm(g, a)
  
          batch_size, d, m = g.size()
          y_dup = torch.repeat_interleave(y, repeats=m, dim=0)
          g_dup = self.g(t, y_dup)
          ga_flat = ga.transpose(1, 2).flatten(0, 1)
          (dg_ga_jvp,) = misc.jvp(
              outputs=g_dup,
              inputs=y_dup,
              grad_inputs=ga_flat,
              create_graph=requires_grad,
              allow_unused=True,
          )
          dg_ga_jvp = dg_ga_jvp.reshape(batch_size, m, d, m).permute(
              0, 2, 1, 3
          )
          dg_ga_jvp = dg_ga_jvp.diagonal(dim1=-2, dim2=-1).sum(-1)
      return dg_ga_jvp
  
imports: import abc
  import torch
  from torch import nn
  from .None import misc
  from ..settings import NOISE_TYPES, SDE_TYPES
  from ..types import Tensor
label_desc: Use image node_id 14 for calling the ForwardSDE obj's underlying member method code with example usage: obj.dg_ga_jvp_column_sum_v2(t, y, a) and returns: dg_ga_jvp