class_docstr: Container for layers of a normalizing flow
class_docstr_tok: ["Container","for","layers","of","a","normalizing","flow"]
code: def inverse(self, u, y):
      sum_log_abs_det_jacobians = 0
      for module in reversed(self):
          u, log_abs_det_jacobian = module.inverse(u, y)
          sum_log_abs_det_jacobians += log_abs_det_jacobian
      return u, sum_log_abs_det_jacobians
  
imports: import copy
  import math
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.distributions import Normal
label_desc: Use image node_id 2 for calling the FlowSequential obj's underlying member method code with example usage: obj.inverse(u, y) and returns: u,  sum_log_abs_det_jacobians