code: def _uniform(
      shape,
      minval=0,
      maxval=None,
      dtype=np.float32,
      seed=None,
      name=None,
  ):  # pylint: disable=unused-argument
      """Numpy uniform random sampler."""
      rng = (
          np.random
          if seed is None
          else np.random.RandomState(seed & 0xFFFFFFFF)
      )
      if minval is not None:
          minval = ops.convert_to_tensor(minval, dtype=dtype)
      if maxval is not None:
          maxval = ops.convert_to_tensor(maxval, dtype=dtype)
      if np.issubdtype(dtype, np.integer):
          if maxval is None:
              if minval is None:
                  return rng.randint(
                      np.iinfo(dtype).min,
                      np.iinfo(dtype).max + 1,
                      size=shape,
                  ).astype(dtype)
              raise ValueError(
                  "Must provide maxval for integer sampling"
              )
          return rng.randint(
              low=minval, high=maxval, size=shape, dtype=dtype
          )
      maxval = 1 if maxval is None else maxval
      shape = _bcast_shape(shape, [minval, maxval])
      return rng.uniform(low=minval, high=maxval, size=shape).astype(
          dtype
      )
  
imports: import numpy
  import numpy
  from tensorflow_probability.python.internal.backend.numpy import _utils
  from tensorflow_probability.python.internal.backend.numpy import ops
  from tensorflow_probability.python.internal.backend.numpy.numpy_math import softmax
label_desc: Use image node_id 16 for calling a global function with example usage: _uniform(shape, minval, maxval, dtype, seed, name) and returns: rng, rng, rng