code: def forward(
      cls, ctx, input, batch_idxs, p=0.5, train=False, inplace=False
  ):
      if p < 0 or p > 1:
          raise ValueError(
              "dropout probability has to be between 0 and 1, "
              "but got {}".format(p)
          )
  
      ctx.p = p
      ctx.train = train
      ctx.inplace = inplace
  
      if ctx.inplace:
          ctx.mark_dirty(input)
          output = input
      else:
          output = input.clone()
  
      if ctx.p > 0 and ctx.train:
          ctx.noise = input.new().resize_(
              batch_idxs.batch_size, input.size(1)
          )
          if ctx.p == 1:
              ctx.noise.fill_(0)
          else:
              ctx.noise.bernoulli_(1 - ctx.p).div_(1 - ctx.p)
          ctx.noise = ctx.noise[batch_idxs.batch_idxs_torch, :]
          output.mul_(ctx.noise)
  
      return output
  
imports: import numpy
  import functools
  import sys
  import torch
  from torch.autograd import Variable
  import torch.nn
  import torch.nn.init
label_desc: Use image node_id 1 for calling the FeatureDropoutFunction obj's underlying member method code with example usage: obj.forward(cls, ctx, input, batch_idxs, p, train, inplace) and returns: output