code: def uniform(
      low=0.0, high=1.0, size=None, dtype=None, device=None, out=None
  ):
      r"""Draw samples from a uniform distribution.
  
      Samples are uniformly distributed over the half-open interval
      ``[low, high)`` (includes low, but excludes high).  In other words,
      any value within the given interval is equally likely to be drawn
      by `uniform`.
  
      Parameters
      ----------
      low : float, ndarray, optional
          Lower boundary of the output interval.  All values generated will be
          greater than or equal to low.  The default value is 0.
      high : float, ndarray, optional
          Upper boundary of the output interval.  All values generated will be
          less than high.  The default value is 1.0.
      size : int or tuple of ints, optional
          Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
          ``m * n * k`` samples are drawn.  If size is ``None`` (default),
          a scalar tensor containing a single value is returned if
          ``low`` and ``high`` are both scalars. Otherwise,
          ``np.broadcast(low, high).size`` samples are drawn.
      dtype : {'float16', 'float32', 'float64'}, optional
          Data type of output samples.
          When npx.is_np_default_dtype() returns False, default dtype is float32;
          When npx.is_np_default_dtype() returns True, default dtype is float64.
      device : Device, optional
          Device context of output. Default is current device.
  
      Returns
      -------
      out : ndarray
          Drawn samples from the parameterized uniform distribution.
  
      See Also
      --------
      randint : Discrete uniform distribution, yielding integers.
      rand : Convenience function that accepts dimensions as input, e.g.,
             ``rand(2,2)`` would generate a 2-by-2 array of floats,
             uniformly distributed over ``[0, 1)``.
  
      Notes
      -----
      The probability density function of the uniform distribution is
  
      .. math:: p(x) = \frac{1}{b - a}
  
      anywhere within the interval ``[a, b)``, and zero elsewhere.
  
      When ``high`` == ``low``, values of ``low`` will be returned.
      If ``high`` < ``low``, the results are officially undefined
      and may eventually raise an error, i.e. do not rely on this
      function to behave when passed arguments satisfying that
      inequality condition.
      """
      return _mx_nd_np.random.uniform(
          low, high, size=size, device=device, dtype=dtype, out=out
      )
  
imports: from ..ndarray import numpy
  from ..random import seed
  from ..util import wrap_ctx_to_device_func
label_desc: Use image node_id 2 for calling a global function with example usage: uniform(low, high, size, dtype, device, out) and returns: _mx_nd_np