def multinomial(logits, num_samples, seed=None, name=None):
  """Draws samples from a multinomial distribution.

  Example:

  ```python
  # samples has shape [1, 5], where each value is either 0 or 1 with equal
  # probability.
  samples = tf.multinomial(tf.log([[10., 10.]]), 5)
  ```

  Args:
    logits: 2-D Tensor with shape `[batch_size, num_classes]`.  Each slice
      `[i, :]` represents the unnormalized log-probabilities for all classes.
    num_samples: 0-D.  Number of independent samples to draw for each row slice.
    seed: A Python integer. Used to create a random seed for the distribution.
      See
      @{tf.set_random_seed}
      for behavior.
    name: Optional name for the operation.

  Returns:
    The drawn samples of shape `[batch_size, num_samples]`.
  """
  with ops.name_scope(name, "multinomial", [logits]):
    logits = ops.convert_to_tensor(logits, name="logits")
    seed1, seed2 = random_seed.get_seed(seed)
    return gen_random_ops.multinomial(
        logits, num_samples, seed=seed1, seed2=seed2)

def random_normal(shape,
                  mean=0.0,
                  stddev=1.0,
                  dtype=dtypes.float32,
                  seed=None,
                  name=None):
  """Outputs random values from a normal distribution.

  Args:
    shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
    mean: A 0-D Tensor or Python value of type `dtype`. The mean of the normal
      distribution.
    stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
      of the normal distribution.
    dtype: The type of the output.
    seed: A Python integer. Used to create a random seed for the distribution.
      See
      @{tf.set_random_seed}
      for behavior.
    name: A name for the operation (optional).

  Returns:
    A tensor of the specified shape filled with random normal values.
  """
  with ops.name_scope(name, "random_normal", [shape, mean, stddev]) as name:
    shape_tensor = _ShapeTensor(shape)
    mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
    stddev_tensor = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
    seed1, seed2 = random_seed.get_seed(seed)
    rnd = gen_random_ops._random_standard_normal(
        shape_tensor, dtype, seed=seed1, seed2=seed2)
    mul = rnd * stddev_tensor
    value = math_ops.add(mul, mean_tensor, name=name)
    return value

def get_seed(seed):
  """Returns the local seeds an operation should use given an op-specific seed.

  See `tf.compat.v1.get_seed` for more details. This wrapper adds support for
  the case
  where `seed` may be a tensor.

  Args:
    seed: An integer or a `tf.int64` scalar tensor.

  Returns:
    A tuple of two `tf.int64` scalar tensors that should be used for the local
    seed of the calling dataset.
  """
  seed, seed2 = random_seed.get_seed(seed)
  if seed is None:
    seed = constant_op.constant(0, dtype=dtypes.int64, name="seed")
  else:
    seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed")
  if seed2 is None:
    seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2")
  else:
    with ops.name_scope("seed2") as scope:
      seed2 = ops.convert_to_tensor(seed2, dtype=dtypes.int64)
      seed2 = array_ops.where(
          math_ops.logical_and(
              math_ops.equal(seed, 0), math_ops.equal(seed2, 0)),
          constant_op.constant(2**31 - 1, dtype=dtypes.int64),
          seed2,
          name=scope)
  return seed, seed2

def multinomial(logits, num_samples, seed=None, name=None):
  """Draws samples from a multinomial distribution.

  Example:

    samples = tf.multinomial(tf.log([[0.5, 0.5]]), 10)
    # samples has shape [1, 10], where each value is either 0 or 1.

    samples = tf.multinomial([[1, -1, -1]], 10)
    # samples is equivalent to tf.zeros([1, 10], dtype=tf.int64).

  Args:
    logits: 2-D Tensor with shape `[batch_size, num_classes]`.  Each slice
      `[i, :]` represents the unnormalized log probabilities for all classes.
    num_samples: 0-D.  Number of independent samples to draw for each row slice.
    seed: A Python integer. Used to create a random seed for the distribution.
      See
      [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
      for behavior.
    name: Optional name for the operation.

  Returns:
    The drawn samples of shape `[batch_size, num_samples]`.
  """
  with ops.op_scope([logits], name, "multinomial"):
    logits = ops.convert_to_tensor(logits, name="logits")
    seed1, seed2 = random_seed.get_seed(seed)
    return gen_random_ops.multinomial(logits, num_samples, seed=seed1,
                                      seed2=seed2)

 def testRandomSeed(self):
   test_cases = [
       # Each test case is a tuple with input to get_seed:
       # (input_graph_seed, input_op_seed)
       # and output from get_seed:
       # (output_graph_seed, output_op_seed)
       ((None, None), (None, None)),
       ((None, 1), (random_seed.DEFAULT_GRAPH_SEED, 1)),
       ((1, 1), (1, 1)),
       ((0, 0), (0, 2**31 - 1)),  # Avoid nondeterministic (0, 0) output
       ((2**31 - 1, 0), (0, 2**31 - 1)),  # Don't wrap to (0, 0) either
       ((0, 2**31 - 1), (0, 2**31 - 1)),  # Wrapping for the other argument
   ]
   if context.executing_eagerly():
     # operation seed is random number generated based on global seed.
     # it's not tested due to possibility of platform or version difference.
     pass
   else:
     # 0 will be the default_graph._lastid.
     test_cases.append(((1, None), (1, 0)))
   for tc in test_cases:
     tinput, toutput = tc[0], tc[1]
     random_seed.set_random_seed(tinput[0])
     g_seed, op_seed = random_seed.get_seed(tinput[1])
     msg = 'test_case = {0}, got {1}, want {2}'.format(tinput,
                                                       (g_seed, op_seed),
                                                       toutput)
     self.assertEqual((g_seed, op_seed), toutput, msg=msg)
     random_seed.set_random_seed(None)

def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=dtypes.float32, seed=None, name=None):
    """Outputs random values from a truncated normal distribution.

  The generated values follow a normal distribution with specified mean and
  standard deviation, except that values whose magnitude is more than 2 standard
  deviations from the mean are dropped and re-picked.

  Args:
    shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
    mean: A 0-D Tensor or Python value of type `dtype`. The mean of the
      truncated normal distribution.
    stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
      of the truncated normal distribution.
    dtype: The type of the output.
    seed: A Python integer. Used to create a random seed for the distribution.
      See
      [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
      for behavior.
    name: A name for the operation (optional).

  Returns:
    A tensor of the specified shape filled with random truncated normal values.
  """
    with ops.name_scope(name, "truncated_normal", [shape, mean, stddev]) as name:
        shape_tensor = _ShapeTensor(shape)
        mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
        stddev_tensor = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
        seed1, seed2 = random_seed.get_seed(seed)
        rnd = gen_random_ops._truncated_normal(shape_tensor, dtype, seed=seed1, seed2=seed2)
        mul = rnd * stddev_tensor
        value = math_ops.add(mul, mean_tensor, name=name)
        return value

def random_uniform(shape,
                   minval=0,
                   maxval=None,
                   dtype=dtypes.float32,
                   seed=None,
                   name=None):
  """Outputs random values from a uniform distribution.

  The generated values follow a uniform distribution in the range
  `[minval, maxval)`. The lower bound `minval` is included in the range, while
  the upper bound `maxval` is excluded.

  For floats, the default range is `[0, 1)`.  For ints, at least `maxval` must
  be specified explicitly.

  In the integer case, the random integers are slightly biased unless
  `maxval - minval` is an exact power of two.  The bias is small for values of
  `maxval - minval` significantly smaller than the range of the output (either
  `2**32` or `2**64`).

  Args:
    shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
    minval: A 0-D Tensor or Python value of type `dtype`. The lower bound on the
      range of random values to generate.  Defaults to 0.
    maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on
      the range of random values to generate.  Defaults to 1 if `dtype` is
      floating point.
    dtype: The type of the output: 'float16`, `float32`, `float64`, `int32`,
      or `int64`.
    seed: A Python integer. Used to create a random seed for the distribution.
      See @{tf.set_random_seed}
      for behavior.
    name: A name for the operation (optional).

  Returns:
    A tensor of the specified shape filled with random uniform values.

  Raises:
    ValueError: If `dtype` is integral and `maxval` is not specified.
  """
  dtype = dtypes.as_dtype(dtype)
  if dtype not in (dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int32,
                   dtypes.int64):
    raise ValueError("Invalid dtype %r" % dtype)
  if maxval is None:
    if dtype.is_integer:
      raise ValueError("Must specify maxval for integer dtype %r" % dtype)
    maxval = 1
  with ops.name_scope(name, "random_uniform", [shape, minval, maxval]) as name:
    shape = _ShapeTensor(shape)
    minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
    maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
    seed1, seed2 = random_seed.get_seed(seed)
    if dtype.is_integer:
      return gen_random_ops._random_uniform_int(
          shape, minval, maxval, seed=seed1, seed2=seed2, name=name)
    else:
      rnd = gen_random_ops._random_uniform(
          shape, dtype, seed=seed1, seed2=seed2)
      return math_ops.add(rnd * (maxval - minval), minval, name=name)

def all_candidate_sampler(true_classes, num_true, num_sampled, unique,
                          seed=None, name=None):
  """Generate the set of all classes.

  Deterministically generates and returns the set of all possible classes.
  For testing purposes.  There is no need to use this, since you might as
  well use full softmax or full logistic regression.

  Args:
    true_classes: A `Tensor` of type `int64` and shape `[batch_size,
      num_true]`. The target classes.
    num_true: An `int`.  The number of target classes per training example.
    num_sampled: An `int`.  The number of possible classes.
    unique: A `bool`. Ignored.
      unique.
    seed: An `int`. An operation-specific seed. Default is 0.
    name: A name for the operation (optional).

  Returns:
    sampled_candidates: A tensor of type `int64` and shape `[num_sampled]`.
      This operation deterministically returns the entire range
      `[0, num_sampled]`.
    true_expected_count: A tensor of type `float`.  Same shape as
      `true_classes`. The expected counts under the sampling distribution
      of each of `true_classes`. All returned values are 1.0.
    sampled_expected_count: A tensor of type `float`. Same shape as
      `sampled_candidates`. The expected counts under the sampling distribution
      of each of `sampled_candidates`. All returned values are 1.0.
  """
  seed1, seed2 = random_seed.get_seed(seed)
  return gen_candidate_sampling_ops._all_candidate_sampler(
      true_classes, num_true, num_sampled, unique, seed=seed1, seed2=seed2,
      name=name)

  def __init__(self,
               input_dataset,
               buffer_size,
               count=None,
               seed=None):
    """See `Dataset.map()` for details."""
    super(_ShuffleAndRepeatDataset, self).__init__()
    self._input_dataset = input_dataset
    self._buffer_size = ops.convert_to_tensor(
        buffer_size, dtype=dtypes.int64, name="buffer_size")
    if count is None:
      self._count = constant_op.constant(-1, dtype=dtypes.int64, name="count")
    else:
      self._count = ops.convert_to_tensor(
          count, dtype=dtypes.int64, name="count")

    seed, seed2 = random_seed.get_seed(seed)
    if seed is None:
      self._seed = constant_op.constant(0, dtype=dtypes.int64, name="seed")
    else:
      self._seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed")
    if seed2 is None:
      self._seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2")
    else:
      self._seed2 = ops.convert_to_tensor(
          seed2, dtype=dtypes.int64, name="seed2")

def random_shuffle(value, seed=None, name=None):
  """Randomly shuffles a tensor along its first dimension.

  The tensor is shuffled along dimension 0, such that each `value[j]` is mapped
  to one and only one `output[i]`. For example, a mapping that might occur for a
  3x2 tensor is:

  ```python
  [[1, 2],       [[5, 6],
   [3, 4],  ==>   [1, 2],
   [5, 6]]        [3, 4]]
  ```

  Args:
    value: A Tensor to be shuffled.
    seed: A Python integer. Used to create a random seed for the distribution.
      See
      @{tf.set_random_seed}
      for behavior.
    name: A name for the operation (optional).

  Returns:
    A tensor of same shape and type as `value`, shuffled along its first
    dimension.
  """
  seed1, seed2 = random_seed.get_seed(seed)
  return gen_random_ops._random_shuffle(
      value, seed=seed1, seed2=seed2, name=name)

  def __init__(self, capacity, min_after_dequeue, dtypes, shapes=None,
               names=None, seed=None, shared_name=None,
               name="random_shuffle_queue"):
    """Create a queue that dequeues elements in a random order.

    A `RandomShuffleQueue` has bounded capacity; supports multiple
    concurrent producers and consumers; and provides exactly-once
    delivery.

    A `RandomShuffleQueue` holds a list of up to `capacity`
    elements. Each element is a fixed-length tuple of tensors whose
    dtypes are described by `dtypes`, and whose shapes are optionally
    described by the `shapes` argument.

    If the `shapes` argument is specified, each component of a queue
    element must have the respective fixed shape. If it is
    unspecified, different queue elements may have different shapes,
    but the use of `dequeue_many` is disallowed.

    The `min_after_dequeue` argument allows the caller to specify a
    minimum number of elements that will remain in the queue after a
    `dequeue` or `dequeue_many` operation completes, to ensure a
    minimum level of mixing of elements. This invariant is maintained
    by blocking those operations until sufficient elements have been
    enqueued. The `min_after_dequeue` argument is ignored after the
    queue has been closed.

    Args:
      capacity: An integer. The upper bound on the number of elements
        that may be stored in this queue.
      min_after_dequeue: An integer (described above).
      dtypes:  A list of `DType` objects. The length of `dtypes` must equal
        the number of tensors in each queue element.
      shapes: (Optional.) A list of fully-defined `TensorShape` objects
        with the same length as `dtypes`, or `None`.
      names: (Optional.) A list of string naming the components in the queue
        with the same length as `dtypes`, or `None`.  If specified the dequeue
        methods return a dictionary with the names as keys.
      seed: A Python integer. Used to create a random seed. See
        [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
        for behavior.
      shared_name: (Optional.) If non-empty, this queue will be shared under
        the given name across multiple sessions.
      name: Optional name for the queue operation.
    """
    dtypes = _as_type_list(dtypes)
    shapes = _as_shape_list(shapes, dtypes)
    names = _as_name_list(names, dtypes)
    # If shared_name is provided and an op seed was not provided, we must ensure
    # that we use the same seed for all queues with the same shared_name.
    if shared_name is not None and seed is None:
      seed = hash(shared_name)
    seed1, seed2 = random_seed.get_seed(seed)
    queue_ref = gen_data_flow_ops._random_shuffle_queue(
        component_types=dtypes, shapes=shapes, capacity=capacity,
        min_after_dequeue=min_after_dequeue, seed=seed1, seed2=seed2,
        shared_name=shared_name, name=name)

    super(RandomShuffleQueue, self).__init__(dtypes, shapes, names, queue_ref)

def random_gamma(shape,
                 alpha,
                 beta=None,
                 dtype=dtypes.float32,
                 seed=None,
                 name=None):
  """Draws `shape` samples from each of the given Gamma distribution(s).

  `alpha` is the shape parameter describing the distribution(s), and `beta` is
  the inverse scale parameter(s).

  Example:

    samples = tf.random_gamma([10], [0.5, 1.5])
    # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
    # the samples drawn from each distribution

    samples = tf.random_gamma([7, 5], [0.5, 1.5])
    # samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
    # represents the 7x5 samples drawn from each of the two distributions

    samples = tf.random_gamma([30], [[1.],[3.],[5.]], beta=[[3., 4.]])
    # samples has shape [30, 3, 2], with 30 samples each of 3x2 distributions.

  Args:
    shape: A 1-D integer Tensor or Python array. The shape of the output samples
      to be drawn per alpha/beta-parameterized distribution.
    alpha: A Tensor or Python value or N-D array of type `dtype`. `alpha`
      provides the shape parameter(s) describing the gamma distribution(s) to
      sample. Must be broadcastable with `beta`.
    beta: A Tensor or Python value or N-D array of type `dtype`. Defaults to 1.
      `beta` provides the inverse scale parameter(s) of the gamma
      distribution(s) to sample. Must be broadcastable with `alpha`.
    dtype: The type of alpha, beta, and the output: `float16`, `float32`, or
      `float64`.
    seed: A Python integer. Used to create a random seed for the distributions.
      See
      [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
      for behavior.
    name: Optional name for the operation.

  Returns:
    samples: a `Tensor` of shape `tf.concat(shape, tf.shape(alpha + beta))` with
      values of type `dtype`.
  """
  with ops.name_scope(name, "random_gamma", [shape, alpha, beta]):
    shape = ops.convert_to_tensor(shape, name="shape", dtype=dtypes.int32)
    alpha = ops.convert_to_tensor(alpha, name="alpha", dtype=dtype)
    beta = ops.convert_to_tensor(beta if beta is not None else 1,
                                 name="beta",
                                 dtype=dtype)
    alpha_broadcast = alpha + array_ops.zeros_like(beta)
    seed1, seed2 = random_seed.get_seed(seed)
    return gen_random_ops._random_gamma(shape,
                                        alpha_broadcast,
                                        seed=seed1,
                                        seed2=seed2) / beta

def log_uniform_candidate_sampler(true_classes, num_true, num_sampled, unique,
                                  range_max, seed=None, name=None):
  """Samples a set of classes using a log-uniform (Zipfian) base distribution.

  This operation randomly samples a tensor of sampled classes
  (`sampled_candidates`) from the range of integers `[0, range_max)`.

  The elements of `sampled_candidates` are drawn without replacement
  (if `unique=True`) or with replacement (if `unique=False`) from
  the base distribution.

  The base distribution for this operation is an approximately log-uniform
  or Zipfian distribution:

  `P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)`

  This sampler is useful when the target classes approximately follow such
  a distribution - for example, if the classes represent words in a lexicon
  sorted in decreasing order of frequency. If your classes are not ordered by
  decreasing frequency, do not use this op.

  In addition, this operation returns tensors `true_expected_count`
  and `sampled_expected_count` representing the number of times each
  of the target classes (`true_classes`) and the sampled
  classes (`sampled_candidates`) is expected to occur in an average
  tensor of sampled classes.  These values correspond to `Q(y|x)`
  defined in [this
  document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
  If `unique=True`, then these are post-rejection probabilities and we
  compute them approximately.

  Args:
    true_classes: A `Tensor` of type `int64` and shape `[batch_size,
      num_true]`. The target classes.
    num_true: An `int`.  The number of target classes per training example.
    num_sampled: An `int`.  The number of classes to randomly sample per batch.
    unique: A `bool`. Determines whether all sampled classes in a batch are
      unique.
    range_max: An `int`. The number of possible classes.
    seed: An `int`. An operation-specific seed. Default is 0.
    name: A name for the operation (optional).

  Returns:
    sampled_candidates: A tensor of type `int64` and shape `[num_sampled]`.
      The sampled classes.
    true_expected_count: A tensor of type `float`.  Same shape as
      `true_classes`. The expected counts under the sampling distribution
      of each of `true_classes`.
    sampled_expected_count: A tensor of type `float`. Same shape as
      `sampled_candidates`. The expected counts under the sampling distribution
      of each of `sampled_candidates`.
  """
  seed1, seed2 = random_seed.get_seed(seed)
  return gen_candidate_sampling_ops._log_uniform_candidate_sampler(
      true_classes, num_true, num_sampled, unique, range_max, seed=seed1,
      seed2=seed2, name=name)

def learned_unigram_candidate_sampler(true_classes, num_true, num_sampled,
                                      unique, range_max, seed=None, name=None):
  """Samples a set of classes from a distribution learned during training.

  This operation randomly samples a tensor of sampled classes
  (`sampled_candidates`) from the range of integers `[0, range_max)`.

  The elements of `sampled_candidates` are drawn without replacement
  (if `unique=True`) or with replacement (if `unique=False`) from
  the base distribution.

  The base distribution for this operation is constructed on the fly
  during training.  It is a unigram distribution over the target
  classes seen so far during training.  Every integer in `[0, range_max)`
  begins with a weight of 1, and is incremented by 1 each time it is
  seen as a target class.  The base distribution is not saved to checkpoints,
  so it is reset when the model is reloaded.

  In addition, this operation returns tensors `true_expected_count`
  and `sampled_expected_count` representing the number of times each
  of the target classes (`true_classes`) and the sampled
  classes (`sampled_candidates`) is expected to occur in an average
  tensor of sampled classes.  These values correspond to `Q(y|x)`
  defined in [this
  document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
  If `unique=True`, then these are post-rejection probabilities and we
  compute them approximately.

  Args:
    true_classes: A `Tensor` of type `int64` and shape `[batch_size,
      num_true]`. The target classes.
    num_true: An `int`.  The number of target classes per training example.
    num_sampled: An `int`.  The number of classes to randomly sample per batch.
    unique: A `bool`. Determines whether all sampled classes in a batch are
      unique.
    range_max: An `int`. The number of possible classes.
    seed: An `int`. An operation-specific seed. Default is 0.
    name: A name for the operation (optional).

  Returns:
    sampled_candidates: A tensor of type `int64` and shape `[num_sampled]`.
      The sampled classes.
    true_expected_count: A tensor of type `float`.  Same shape as
      `true_classes`. The expected counts under the sampling distribution
      of each of `true_classes`.
    sampled_expected_count: A tensor of type `float`. Same shape as
      `sampled_candidates`. The expected counts under the sampling distribution
      of each of `sampled_candidates`.

  """
  seed1, seed2 = random_seed.get_seed(seed)
  return gen_candidate_sampling_ops._learned_unigram_candidate_sampler(
      true_classes, num_true, num_sampled, unique, range_max, seed=seed1,
      seed2=seed2, name=name)

def uniform_candidate_sampler(true_classes, num_true, num_sampled, unique,
                              range_max, seed=None, name=None):
  """Samples a set of classes using a uniform base distribution.

  This operation randomly samples a tensor of sampled classes
  (`sampled_candidates`) from the range of integers `[0, range_max)`.

  The elements of `sampled_candidates` are drawn without replacement
  (if `unique=True`) or with replacement (if `unique=False`) from
  the base distribution.

  The base distribution for this operation is the uniform distribution
  over the range of integers `[0, range_max)`.

  In addition, this operation returns tensors `true_expected_count`
  and `sampled_expected_count` representing the number of times each
  of the target classes (`true_classes`) and the sampled
  classes (`sampled_candidates`) is expected to occur in an average
  tensor of sampled classes.  These values correspond to `Q(y|x)`
  defined in [this
  document](http://www.tensorflow.org/extras/candidate_sampling.pdf).
  If `unique=True`, then these are post-rejection probabilities and we
  compute them approximately.

  Args:
    true_classes: A `Tensor` of type `int64` and shape `[batch_size,
      num_true]`. The target classes.
    num_true: An `int`.  The number of target classes per training example.
    num_sampled: An `int`.  The number of classes to randomly sample. The
      `sampled_candidates` return value will have shape `[num_sampled]`. If
      `unique=True`, `num_sampled` must be less than or equal to `range_max`.
    unique: A `bool`. Determines whether all sampled classes in a batch are
      unique.
    range_max: An `int`. The number of possible classes.
    seed: An `int`. An operation-specific seed. Default is 0.
    name: A name for the operation (optional).

  Returns:
    sampled_candidates: A tensor of type `int64` and shape `[num_sampled]`.  The
      sampled classes, either with possible duplicates (`unique=False`) or all
      unique (`unique=True`). In either case, `sampled_candidates` is
      independent of the true classes.
    true_expected_count: A tensor of type `float`.  Same shape as
      `true_classes`. The expected counts under the sampling distribution
      of each of `true_classes`.
    sampled_expected_count: A tensor of type `float`. Same shape as
      `sampled_candidates`. The expected counts under the sampling distribution
      of each of `sampled_candidates`.
  """
  seed1, seed2 = random_seed.get_seed(seed)
  return gen_candidate_sampling_ops.uniform_candidate_sampler(
      true_classes, num_true, num_sampled, unique, range_max, seed=seed1,
      seed2=seed2, name=name)

  def __init__(self,
               images,
               labels,
               dtype=dtypes.float32,
               seed=None):

    self.check_data(images, labels)
    seed1, seed2 = random_seed.get_seed(seed)
    self._images = images
    self._labels = labels
    self._epochs_completed = 0
    self._index_in_epoch = 0
    self._total_batches = images.shape[0]

 def __init__(self, seed=None):
   """A `Dataset` of pseudorandom values."""
   super(RandomDataset, self).__init__()
   seed, seed2 = random_seed.get_seed(seed)
   if seed is None:
     self._seed = constant_op.constant(0, dtype=dtypes.int64, name="seed")
   else:
     self._seed = ops.convert_to_tensor(seed, dtype=dtypes.int64, name="seed")
   if seed2 is None:
     self._seed2 = constant_op.constant(0, dtype=dtypes.int64, name="seed2")
   else:
     self._seed2 = ops.convert_to_tensor(
         seed2, dtype=dtypes.int64, name="seed2")

def parameterized_truncated_normal(shape,
                                   means=0.0,
                                   stddevs=1.0,
                                   minvals=-2.0,
                                   maxvals=2.0,
                                   dtype=dtypes.float32,
                                   seed=None,
                                   name=None):
  """Outputs random values from a truncated normal distribution.

  The generated values follow a normal distribution with specified mean and
  standard deviation, except that values whose magnitude is more than 2 standard
  deviations from the mean are dropped and re-picked.

  Args:
    shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
    means: A 0-D Tensor or Python value of type `dtype`. The mean of the
      truncated normal distribution.
    stddevs: A 0-D Tensor or Python value of type `dtype`. The standard
      deviation of the truncated normal distribution.
    minvals: A 0-D Tensor or Python value of type `dtype`. The minimum value of
      the truncated normal distribution.
    maxvals: A 0-D Tensor or Python value of type `dtype`. The maximum value of
      the truncated normal distribution.
    dtype: The type of the output.
    seed: A Python integer. Used to create a random seed for the distribution.
      See
      @{tf.set_random_seed}
      for behavior.
    name: A name for the operation (optional).

  Returns:
    A tensor of the specified shape filled with random truncated normal values.
  """
  with ops.name_scope(name, "parameterized_truncated_normal",
                      [shape, means, stddevs, minvals, maxvals]) as name:
    shape_tensor = _ShapeTensor(shape)
    means_tensor = ops.convert_to_tensor(means, dtype=dtype, name="means")
    stddevs_tensor = ops.convert_to_tensor(stddevs, dtype=dtype, name="stddevs")
    minvals_tensor = ops.convert_to_tensor(minvals, dtype=dtype, name="minvals")
    maxvals_tensor = ops.convert_to_tensor(maxvals, dtype=dtype, name="maxvals")
    seed1, seed2 = random_seed.get_seed(seed)
    rnd = gen_random_ops._parameterized_truncated_normal(
        shape_tensor,
        means_tensor,
        stddevs_tensor,
        minvals_tensor,
        maxvals_tensor,
        seed=seed1,
        seed2=seed2)
    return rnd

  def __init__(self,
               rnn_mode,
               num_layers,
               num_units,
               input_size,
               input_mode="linear_input",
               direction=CUDNN_RNN_UNIDIRECTION,
               dtype=dtypes.float32,
               dropout=0.,
               seed=0):
    """Creates a CudnnRNN model from model spec.

    Args:
      rnn_mode: a string specifies the mode, under which this RNN model runs.
          Could be either 'lstm', 'gru', 'rnn_tanh' or 'rnn_relu'.
      num_layers: the number of layers for the RNN model.
      num_units: the number of units within the RNN model.
      input_size: the size of the input, it could be different from the
          num_units.
      input_mode: indicate whether there is a linear projection between the
          input and the actual computation before the first layer. It could be
          'linear_input', 'skip_input' or 'auto_select'.
          'linear_input' (default) always applies a linear projection of input
          onto RNN hidden state. (standard RNN behavior).
          'skip_input' is only allowed when input_size == num_units;
          'auto_select' implies 'skip_input' when input_size == num_units;
          otherwise, it implies 'linear_input'.
      direction: the direction model that the model operates. Could be either
          'unidirectional' or 'bidirectional'
      dtype: dtype of params, tf.float32 or tf.float64.
      dropout: whether to enable dropout. With it is 0, dropout is disabled.
      seed: the op seed used for initializing dropout. See @{tf.set_random_seed}
          for behavior.
    Raises:
      ValueError: if direction is invalid.
    """
    if direction not in (CUDNN_RNN_UNIDIRECTION, CUDNN_RNN_BIDIRECTION):
      raise ValueError("Invalid direction: %s, expect %s or %s",
                       direction, CUDNN_RNN_UNIDIRECTION, CUDNN_RNN_BIDIRECTION)
    self._num_layers = num_layers
    self._num_units = num_units
    self._input_size = input_size
    self._rnn_mode = rnn_mode
    self._input_mode = input_mode
    self._direction = direction
    self._dtype = dtype
    self._dropout = dropout
    # get graph and op seed.
    self._seed, self._seed2 = random_seed.get_seed(seed)
    if self._seed is None and self._seed2 is None:
      self._seed, self._seed2 = 0, 0

 def __init__(self, data_dir='./data/', split="val", top_answers=3000,
              max_ques_len=15, seed=None):
     self.data_dir = data_dir
     self.split = split
     self.img_dir = self.data_dir + "{}2014/".format(self.split)
     self.top_answers = top_answers
     self.max_ques_len = max_ques_len
     self._data = self.preprocess_json(self.split)
     self.question_to_index = self.map_to_index(top=None, answer=False)
     self.vocab_size = len(self.question_to_index)
     self.answer_to_index = self.map_to_index(top=self.top_answers)
     self._num_examples = len(self._data)
     self._epochs_completed = 0
     self._index_in_epoch = 0
     self.number_of_questions = len(self._data)
     seed1, seed2 = random_seed.get_seed(seed)
     np.random.seed(seed1 if seed is None else seed2)