tensorflow_probability/python/edward2/random_variable.py
def eval(self, session=None, feed_dict=None):
    """In a session, computes and returns the value of this random variable.

    This is not a graph construction method, it does not add ops to the graph.

    This convenience method requires a session where the graph
    containing this variable has been launched. If no session is
    passed, the default session is used.

    Args:
      session: tf.BaseSession.
        The `tf.Session` to use to evaluate this random variable. If
        none, the default session is used.
      feed_dict: dict.
        A dictionary that maps `tf.Tensor` objects to feed values. See
        `tf.Session.run()` for a description of the valid feed values.

    Returns:
      Value of the random variable.

    #### Examples

    ```python
    x = Normal(0.0, 1.0)
    with tf.Session() as sess:
      # Usage passing the session explicitly.
      print(x.eval(sess))
      # Usage with the default session.  The 'with' block
      # above makes 'sess' the default session.
      print(x.eval())
    ```
    """
    return self.value.eval(session=session, feed_dict=feed_dict)
def eval(self, session=None, feed_dict=None):
    """In a session, computes and returns the value of this random variable.

    This is not a graph construction method, it does not add ops to the graph.

    This convenience method requires a session where the graph
    containing this variable has been launched. If no session is
    passed, the default session is used.

    Args:
      session: tf.BaseSession.
        The `tf.Session` to use to evaluate this random variable. If
        none, the default session is used.
      feed_dict: dict.
        A dictionary that maps `tf.Tensor` objects to feed values. See
        `tf.Session.run()` for a description of the valid feed values.

    Returns:
      Value of the random variable.

    #### Examples

    ```python
    x = Normal(0.0, 1.0)
    with tf.Session() as sess:
      # Usage passing the session explicitly.
      print(x.eval(sess))
      # Usage with the default session.  The 'with' block
      # above makes 'sess' the default session.
      print(x.eval())
    ```
    """
    return self.value.eval(session=session, feed_dict=feed_dict)
def eval ( self , session = None , feed_dict = None ) : return self . value . eval ( session = session , feed_dict = feed_dict )
In a session, computes and returns the value of this random variable.

    This is not a graph construction method, it does not add ops to the graph.

    This convenience method requires a session where the graph
    containing this variable has been launched. If no session is
    passed, the default session is used.

    Args:
      session: tf.BaseSession.
        The `tf.Session` to use to evaluate this random variable. If
        none, the default session is used.
      feed_dict: dict.
        A dictionary that maps `tf.Tensor` objects to feed values. See
        `tf.Session.run()` for a description of the valid feed values.

    Returns:
      Value of the random variable.

    #### Examples

    ```python
    x = Normal(0.0, 1.0)
    with tf.Session() as sess:
      # Usage passing the session explicitly.
      print(x.eval(sess))
      # Usage with the default session.  The 'with' block
      # above makes 'sess' the default session.
      print(x.eval())
    ```
In a session computes and returns the value of this random variable .