code: def uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None):
      dtype = dtype or floatx()
      dtype = to_torch_dtype(dtype)
      requested_shape = shape
      if len(requested_shape) == 0:
          shape = (1,)
      # Do not use generator during symbolic execution.
      if get_device() == "meta":
          rand_tensor = torch.rand(
              size=shape, dtype=dtype, device=get_device()
          )
      else:
          generator = torch_seed_generator(seed)
          rand_tensor = torch.rand(
              size=shape,
              generator=generator,
              dtype=dtype,
              device=get_device(),
          )
  
      output = (maxval - minval) * rand_tensor + minval
  
      if len(requested_shape) == 0:
          return output[0]
      return output
  
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 4 for calling a global function with example usage: uniform(shape, minval, maxval, dtype, seed) and returns: output, output