class_docstr: Bijector that computes the logistic sigmoid function.
  
  If the `low` and `high` parameters are not passed, the transformation is
  equivalent to `Y = g(X) = 1 / (1 + exp(-X))`, the same as `tf.sigmoid`.
  
  If the `low` and `high` parameters are passed, the transformation is
  equivalent to `low + (high - low) * g(X)` (with `g(X)` as defined above),
  a sigmoid that is shifted and scaled along the output axis. This is
  implemented as `high * g(X) + low * g(-X)`, which is more numerically
  stable than direct shifting and scaling.
  
  Specifically, `low + (high - low) * g(X)` can sometimes evalaute
  to slightly larger than `high`, which would trigger assertions
  elsewhere.  The formula `high * g(X) + low * g(-X)` doesn't do that.
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docstr: Initialize a `Sigmoid` bijector.
  
  Args:
    low: Floating point tensor or `None`, lower boundary of the output
      interval. If `None`, the implied default is `0.`. Either both or neither
      of `low` and `high` must be specified. If specified, Must have
      `low < high`.
    high: Floating point tensor or `None`, upper boundary of the output
      interval. if `None`, the implied default is `1.`. Either both or neither
      of `low` and `high` must be specified. If specified, must have
      `low < high`.
    validate_args: Python `bool`, default `False`. When `True` bijector
      parameters are checked for validity despite possibly degrading runtime
      performance. When `False` invalid inputs may silently render incorrect
      outputs.
    name: Python `str` name prefixed to Ops created by this class.
  
  Raises:
    ValueError: If exactly one of `low` and `high` is specified.
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code: def __init__(
      self, low=None, high=None, validate_args=False, name="sigmoid"
  ):
      """Initialize a `Sigmoid` bijector.
  
      Args:
        low: Floating point tensor or `None`, lower boundary of the output
          interval. If `None`, the implied default is `0.`. Either both or neither
          of `low` and `high` must be specified. If specified, Must have
          `low < high`.
        high: Floating point tensor or `None`, upper boundary of the output
          interval. if `None`, the implied default is `1.`. Either both or neither
          of `low` and `high` must be specified. If specified, must have
          `low < high`.
        validate_args: Python `bool`, default `False`. When `True` bijector
          parameters are checked for validity despite possibly degrading runtime
          performance. When `False` invalid inputs may silently render incorrect
          outputs.
        name: Python `str` name prefixed to Ops created by this class.
  
      Raises:
        ValueError: If exactly one of `low` and `high` is specified.
      """
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          if low is None and high is None:
              self._is_standard_sigmoid = True
          elif low is not None and high is not None:
              self._is_standard_sigmoid = False
          else:
              raise ValueError(
                  "Either both or neither of `low` and `high` must be passed. "
                  "Received `low={}`, `high={}`".format(low, high)
              )
  
          dtype = dtype_util.common_dtype(
              [low, high], dtype_hint=tf.float32
          )
          self._low = tensor_util.convert_nonref_to_tensor(
              low, dtype=dtype
          )
          self._high = tensor_util.convert_nonref_to_tensor(
              high, dtype=dtype
          )
          super(Sigmoid, self).__init__(
              forward_min_event_ndims=0,
              validate_args=validate_args,
              parameters=parameters,
              name=name,
          )
  
file_path: probability/tensorflow_probability/python/bijectors/sigmoid.py
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import tensor_util
label_desc: Use image node_id 1 to create a new Sigmoid object from inherited base classes: bijector, bijector with example: obj = Sigmoid(low, high, validate_args, name)