code: def randint(shape, minval, maxval, dtype="int32", seed=None):
      dtype = to_torch_dtype(dtype)
      # Do not use generator during symbolic execution.
      if get_device() == "meta":
          return torch.randint(
              low=minval,
              high=maxval,
              size=shape,
              dtype=dtype,
              device=get_device(),
          )
      generator = torch_seed_generator(seed)
      return torch.randint(
          low=minval,
          high=maxval,
          size=shape,
          generator=generator,
          dtype=dtype,
          device=get_device(),
      )
  
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 5 for calling a global function with example usage: randint(shape, minval, maxval, dtype, seed) and returns: torch, torch