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.
      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.
      out : ``ndarray``, optional
          Store output to an existing ``ndarray``.
  
      Returns
      -------
      out : ndarray
          Drawn samples from the parameterized uniform distribution.
      """
      if device is None:
          device = str(current_device())
      else:
          device = str(device)
      if dtype is not None and not isinstance(dtype, str):
          dtype = get_dtype_name(dtype)
      if size == ():
          size = None
      return _api_internal.uniform(low, high, size, device, dtype, out)
  
imports: import numpy
  from ...util import is_np_default_dtype
  from ...device import current_device
  from .None import _internal
  from .None import _api_internal
  from ...util import wrap_ctx_to_device_func
  from ..ndarray import get_dtype_name
label_desc: Use image node_id 2 for calling a global function with example usage: uniform(low, high, size, dtype, device, out) and returns: _api_internal