code: def random_sample(
      random_state, size=None, chunk_size=None, gpu=None, dtype=None
  ):
      """
      Return random floats in the half-open interval [0.0, 1.0).
  
      Results are from the "continuous uniform" distribution over the
      stated interval.  To sample :math:`Unif[a, b), b > a` multiply
      the output of `random_sample` by `(b-a)` and add `a`::
  
        (b - a) * random_sample() + a
  
      Parameters
      ----------
      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.  Default is None, in which case a
          single value is returned.
      chunk_size : int or tuple of int or tuple of ints, optional
          Desired chunk size on each dimension
      gpu : bool, optional
          Allocate the tensor on GPU if True, False as default
      dtype : data-type, optional
        Data-type of the returned tensor.
  
      Returns
      -------
      out : float or Tensor of floats
          Array of random floats of shape `size` (unless ``size=None``, in which
          case a single float is returned).
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> mt.random.random_sample().execute()
      0.47108547995356098
      >>> type(mt.random.random_sample().execute())
      <type 'float'>
      >>> mt.random.random_sample((5,)).execute()
      array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428])
  
      Three-by-two array of random numbers from [-5, 0):
  
      >>> (5 * mt.random.random_sample((3, 2)) - 5).execute()
      array([[-3.99149989, -0.52338984],
             [-2.99091858, -0.79479508],
             [-1.23204345, -1.75224494]])
      """
      if dtype is None:
          dtype = np.dtype("f8")
      size = random_state._handle_size(size)
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorRandomSample(
          seed=seed, size=size, gpu=gpu, dtype=dtype
      )
      return op(chunk_size=chunk_size)
  
file_path: mars/mars/tensor/random/random_sample.py
imports: import numpy
  from ...None import opcodes
  from ..utils import gen_random_seeds
  from .core import TensorRandomOperandMixin, TensorSimpleRandomData
label_desc: Use image node_id 1 for calling a global function with example usage: random_sample(random_state, size, chunk_size, gpu, dtype) and returns: op