text: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  # Copyright 1999-2021 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 TensorExponential(TensorDistribution, TensorRandomOperandMixin):
      _input_fields_ = ['_scale']
      _op_type_ = OperandDef.RAND_EXPONENTIAL
  
      _fields_ = '_scale', '_size'
      _scale = AnyField('scale')
      _func_name = 'exponential'
  
      @property
      def scale(self):
          return self._scale
  
      def __init__(self, size=None, dtype=None, **kw):
          dtype = np.dtype(dtype) if dtype is not None else dtype
          super().__init__(_size=size, dtype=dtype, **kw)
  
      def __call__(self, scale, chunk_size=None):
          return self.new_tensor([scale], self._size, raw_chunk_size=chunk_size)
  
  
  def exponential(random_state, scale=1.0, size=None, chunk_size=None, gpu=None, dtype=None):
      r"""
      Draw samples from an exponential distribution.
  
      Its probability density function is
  
      .. math:: f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}),
  
      for ``x > 0`` and 0 elsewhere. :math:`\beta` is the scale parameter,
      which is the inverse of the rate parameter :math:`\lambda = 1/\beta`.
      The rate parameter is an alternative, widely used parameterization
      of the exponential distribution [3]_.
  
      The exponential distribution is a continuous analogue of the
      geometric distribution.  It describes many common situations, such as
      the size of raindrops measured over many rainstorms [1]_, or the time
      between page requests to Wikipedia [2]_.
  
      Parameters
      ----------
      scale : float or array_like of floats
          The scale parameter, :math:`\beta = 1/\lambda`.
      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 ``scale`` is a scalar.  Otherwise,
          ``np.array(scale).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 exponential distribution.
  
      References
      ----------
      .. [1] Peyton Z. Peebles Jr., "Probability, Random Variables and
             Random Signal Principles", 4th ed, 2001, p. 57.
      .. [2] Wikipedia, "Poisson process",
             http://en.wikipedia.org/wiki/Poisson_process
      .. [3] Wikipedia, "Exponential distribution",
             http://en.wikipedia.org/wiki/Exponential_distribution
      """
      if dtype is None:
          dtype = np.random.RandomState().exponential(
              handle_array(scale), size=(0,)).dtype
      size = random_state._handle_size(size)
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorExponential(seed=seed, size=size, gpu=gpu, dtype=dtype)
      return op(scale, chunk_size=chunk_size)
  