code: def stateless_random_uniform(
      shape,
      seed,
      minval=0,
      maxval=None,
      dtype=dtypes.float32,
      name=None,
      alg="auto_select",
  ):
      """Outputs deterministic pseudorandom values from a uniform distribution.
  
      This is a stateless version of `tf.random.uniform`: if run twice with the
      same seeds and shapes, it will produce the same pseudorandom numbers.  The
      output is consistent across multiple runs on the same hardware (and between
      CPU and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
      hardware.
  
      The generated values follow a uniform distribution in the range
      `[minval, maxval)`. The lower bound `minval` is included in the range, while
      the upper bound `maxval` is excluded.
  
      For floats, the default range is `[0, 1)`.  For ints, at least `maxval` must
      be specified explicitly.
  
      In the integer case, the random integers are slightly biased unless
      `maxval - minval` is an exact power of two.  The bias is small for values of
      `maxval - minval` significantly smaller than the range of the output (either
      `2**32` or `2**64`).
  
      For full-range (i.e. inclusive of both max and min) random integers, pass
      `minval=None` and `maxval=None` with an integer `dtype`. For an integer dtype
      either both `minval` and `maxval` must be `None` or neither may be `None`. For
      example:
      ```python
      ints = tf.random.stateless_uniform(
          [10], seed=(2, 3), minval=None, maxval=None, dtype=tf.int32)
      ```
  
      Args:
        shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
        seed: A shape [2] Tensor, the seed to the random number generator. Must have
          dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
        minval: A Tensor or Python value of type `dtype`, broadcastable with `shape`
          (for integer types, broadcasting is not supported, so it needs to be a
          scalar). The lower bound on the range of random values to generate. Pass
          `None` for full-range integers.  Defaults to 0.
        maxval: A Tensor or Python value of type `dtype`, broadcastable with `shape`
          (for integer types, broadcasting is not supported, so it needs to be a
          scalar). The upper bound on the range of random values to generate.
          Defaults to 1 if `dtype` is floating point. Pass `None` for full-range
          integers.
        dtype: The type of the output: `float16`, `bfloat16`, `float32`, `float64`,
          `int32`, or `int64`. For unbounded uniform ints (`minval`, `maxval` both
          `None`), `uint32` and `uint64` may be used. Defaults to `float32`.
        name: A name for the operation (optional).
        alg: The RNG algorithm used to generate the random numbers. Valid choices
          are `"philox"` for [the Philox
          algorithm](https://www.thesalmons.org/john/random123/papers/random123sc11.pdf),
          `"threefry"` for [the ThreeFry
          algorithm](https://www.thesalmons.org/john/random123/papers/random123sc11.pdf),
          and `"auto_select"` (default) for the system to automatically select an
          algorithm based the device type. Values of `tf.random.Algorithm` can also
          be used. Note that with `"auto_select"`, the outputs of this function may
          change when it is running on a different device.
  
      Returns:
        A tensor of the specified shape filled with random uniform values.
  
      Raises:
        ValueError: If `dtype` is integral and only one of `minval` or `maxval` is
          specified.
      """
      dtype = dtypes.as_dtype(dtype)
      accepted_dtypes = (
          dtypes.float16,
          dtypes.bfloat16,
          dtypes.float32,
          dtypes.float64,
          dtypes.int32,
          dtypes.int64,
          dtypes.uint32,
          dtypes.uint64,
      )
      if dtype not in accepted_dtypes:
          raise ValueError(
              f"Argument `dtype` got invalid value {dtype}. Accepted dtypes are "
              f"{accepted_dtypes}."
          )
      if dtype.is_integer:
          if (minval is None) != (maxval is None):
              raise ValueError(
                  f"For integer `dtype` argument {dtype}, argument `minval` and "
                  f"`maxval` must be both None or not None. Got `minval`={minval} and "
                  f"`maxval`={maxval}."
              )
          if minval is not None and dtype in (
              dtypes.uint32,
              dtypes.uint64,
          ):
              raise ValueError(
                  f"Argument `dtype` got invalid value {dtype} when argument `minval` "
                  "is not None. Please don't use unsigned integers in this case."
              )
      elif maxval is None:
          maxval = 1
      with ops.name_scope(
          name,
          "stateless_random_uniform",
          [shape, seed, minval, maxval],
      ) as name:
          shape = shape_util.shape_tensor(shape)
          if dtype.is_integer and minval is None:
              key, counter, alg = random_ops_util.get_key_counter_alg(
                  seed, alg
              )
              result = gen_stateless_random_ops_v2.stateless_random_uniform_full_int_v2(
                  shape,
                  key=key,
                  counter=counter,
                  dtype=dtype,
                  alg=alg,
                  name=name,
              )
          else:
              minval = ops.convert_to_tensor(
                  minval, dtype=dtype, name="min"
              )
              maxval = ops.convert_to_tensor(
                  maxval, dtype=dtype, name="max"
              )
              if dtype.is_integer:
                  (
                      key,
                      counter,
                      alg,
                  ) = random_ops_util.get_key_counter_alg(seed, alg)
                  result = gen_stateless_random_ops_v2.stateless_random_uniform_int_v2(
                      shape,
                      key=key,
                      counter=counter,
                      minval=minval,
                      maxval=maxval,
                      alg=alg,
                      name=name,
                  )
              else:
                  (
                      key,
                      counter,
                      alg,
                  ) = random_ops_util.get_key_counter_alg(seed, alg)
                  rnd = gen_stateless_random_ops_v2.stateless_random_uniform_v2(
                      shape,
                      key=key,
                      counter=counter,
                      dtype=dtype,
                      alg=alg,
                  )
                  result = math_ops.add(
                      rnd * (maxval - minval), minval, name=name
                  )
          shape_util.maybe_set_static_shape(result, shape)
          return result
  
imports: import numpy
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import gen_random_index_shuffle_ops
  from tensorflow.python.ops import gen_stateless_random_ops
  from tensorflow.python.ops import gen_stateless_random_ops_v2
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops_util
  from tensorflow.python.ops import shape_util
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 5 for calling a global function with example usage: stateless_random_uniform(shape, seed, minval, maxval, dtype, name, alg) and returns: result