text: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  # Copyright 1999-2020 Alibaba Group Holding Ltd.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import numpy as np
  
  from ... import opcodes as OperandDef
  from ...serialization.serializables import AnyField
  from ..utils import gen_random_seeds
  from .core import TensorRandomOperandMixin, handle_array, TensorDistribution
  
  
  class TensorUniform(TensorDistribution, TensorRandomOperandMixin):
      _input_fields_ = ['_low', '_high']
      _op_type_ = OperandDef.RAND_UNIFORM
  
      _fields_ = '_low', '_high', '_size'
      _low = AnyField('low')
      _high = AnyField('high')
      _func_name = 'uniform'
  
      def __init__(self, size=None, state=None, dtype=None, **kw):
          dtype = np.dtype(dtype) if dtype is not None else dtype
          super().__init__(_size=size, _state=state, dtype=dtype, **kw)
  
      @property
      def low(self):
          return self._low
  
      @property
      def high(self):
          return self._high
  
      def __call__(self, low, high, chunk_size=None):
          return self.new_tensor([low, high], None, raw_chunk_size=chunk_size)
  
  
  def uniform(random_state, low=0.0, high=1.0, size=None, chunk_size=None, gpu=None, dtype=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 or array_like of floats, 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 or array_like of floats
          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 single value is returned if ``low`` and ``high`` are both scalars.
          Otherwise, ``mt.broadcast(low, high).size`` samples are drawn.
      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 : Tensor or scalar
          Drawn samples from the parameterized uniform distribution.
  
      See Also
      --------
      randint : Discrete uniform distribution, yielding integers.
      random_integers : Discrete uniform distribution over the closed
                        interval ``[low, high]``.
      random_sample : Floats uniformly distributed over ``[0, 1)``.
      random : Alias for `random_sample`.
      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.
  
      Examples
      --------
      Draw samples from the distribution:
  
      >>> import mars.tensor as mt
  
      >>> s = mt.random.uniform(-1,0,1000)
  
      All values are within the given interval:
  
      >>> mt.all(s >= -1).execute()
      True
      >>> mt.all(s < 0).execute()
      True
  
      Display the histogram of the samples, along with the
      probability density function:
  
      >>> import matplotlib.pyplot as plt
      >>> count, bins, ignored = plt.hist(s.execute(), 15, normed=True)
      >>> plt.plot(bins, mt.ones_like(bins).execute(), linewidth=2, color='r')
      >>> plt.show()
      """
      if dtype is None:
          dtype = np.random.RandomState().uniform(
              handle_array(low), handle_array(high), size=(0,)).dtype
      size = random_state._handle_size(size)
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorUniform(size=size, seed=seed, gpu=gpu, dtype=dtype)
      return op(low, high, chunk_size=chunk_size)
  