code: def forward(ctx, input, 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 = torch.empty(
              (input.size(0), input.size(-1)),
              dtype=input.dtype,
              layout=input.layout,
              device=input.device,
          )
          if ctx.p == 1:
              ctx.noise.fill_(0)
          else:
              ctx.noise.bernoulli_(1 - ctx.p).div_(1 - ctx.p)
          ctx.noise = ctx.noise[:, None, :]
          output.mul_(ctx.noise)
  
      return output
  
imports: import copy
  import math
  import torch
  import torch.nn
  import torch.nn.functional
  from stanza.models.constituency.positional_encoding import ConcatSinusoidalEncoding
label_desc: Use image node_id 1 for calling the FeatureDropoutFunction obj's underlying member method code with example usage: obj.forward(ctx, input, p, train, inplace) and returns: output