code: def dropout(inputs, rate, noise_shape=None, seed=None):
      if (
          seed is not None
          and not (
              isinstance(seed, SeedGenerator)
              and seed._initial_seed is None
          )
          or noise_shape is not None
      ):
          keep_prob = 1.0 - rate
          noise_shape = _get_concrete_noise_shape(inputs, noise_shape)
          keep_prob_matrix = torch.full(
              noise_shape, keep_prob, device=get_device()
          )
          generator = torch_seed_generator(seed)
  
          # Do not use generator during symbolic execution.
          if get_device() == "meta":
              mask = torch.bernoulli(keep_prob_matrix)
          else:
              mask = torch.bernoulli(
                  keep_prob_matrix, generator=generator
              )
  
          mask = mask.bool()
          mask = torch.broadcast_to(mask, inputs.shape)
          return torch.where(
              mask,
              inputs / keep_prob,
              torch.zeros_like(inputs, dtype=inputs.dtype),
          )
      # Fast path, unseeded (since torch doesn't support seeding dropout!!!!)
      # Using the above implementation is possible, but much slower.
      return torch.nn.functional.dropout(
          inputs, p=rate, training=True, inplace=False
      )
  
imports: import torch
  import torch._dynamo
  import torch.nn.functional
  from keras.backend.config import floatx
  from keras.backend.torch.core import convert_to_tensor
  from keras.backend.torch.core import get_device
  from keras.backend.torch.core import to_torch_dtype
  from keras.random.seed_generator import SeedGenerator
  from keras.random.seed_generator import draw_seed
  from keras.random.seed_generator import make_default_seed
label_desc: Use image node_id 8 for calling a global function with example usage: dropout(inputs, rate, noise_shape, seed) and returns: torch, torch