
                   initializer=None, regularizer=None,
                   trainable=True, constraint=None):
    """Adds a new variable to the layer, or gets an existing one; returns it.

    Arguments:
      name: variable name.
      shape: variable shape.
      dtype: The type of the variable. Defaults to `self.dtype` or `float32`.
      initializer: initializer instance (callable).
      regularizer: regularizer instance (callable).
      trainable: whether the variable should be part of the layer's
        "trainable_variables" (e.g. variables, biases)
        or "non_trainable_variables" (e.g. BatchNorm mean, stddev).
      constraint: constraint instance (callable).

    Returns:
      The created variable.

    Raises:
      RuntimeError: If called in Eager mode with regularizers.
    """
    # Note that we currently don't support variable regularization in Eager
    # mode. An alternative is for users to directly compute these losses before
    # performing a backward pass.
    if context.in_graph_mode():
      existing_variables = set(tf_variables.global_variables())
    else:
      existing_variables = []
      if regularizer is not None:
        raise RuntimeError('Variable regularization not supported in Eager '
                          'mode.')
    if dtype is None:
      dtype = self.dtype or dtypes.float32

    self._set_scope(None)
    with vs.variable_scope(
        self._scope, reuse=(self.built or self._reuse)) as scope:
      with ops.name_scope(scope.original_name_scope):
        variable = vs.get_variable(name,
                                   shape=shape,
                                   initializer=initializer,
                                   dtype=dtypes.as_dtype(dtype),
                                   constraint=constraint,
                                   trainable=trainable and self.trainable)
        if variable in existing_variables:
          return variable
        if regularizer:
          # To match the behavior of tf.get_variable(), we only
          # apply regularization if the variable is newly created.
          if isinstance(variable, tf_variables.PartitionedVariable):
            for v in variable:
              with ops.colocate_with(v.op):
                with ops.name_scope(name + '/Regularizer'):
                  regularization = regularizer(v)
              if regularization is not None:
                self.add_loss(regularization)
                _add_elements_to_collection(
                    regularization, ops.GraphKeys.REGULARIZATION_LOSSES)
          else:
            with ops.colocate_with(variable.op):
              with ops.name_scope(name + '/Regularizer'):
                regularization = regularizer(variable)
            if regularization is not None:
              self.add_loss(regularization)
              _add_elements_to_collection(
                  regularization, ops.GraphKeys.REGULARIZATION_LOSSES)
    if trainable:
      self._trainable_weights.append(variable)
    else:
      self._non_trainable_weights.append(variable)
    return variable

  def __call__(self, inputs, *args, **kwargs):
    """Wraps `call`, applying pre- and post-processing steps.

    Arguments:
      inputs: input tensor(s).
      *args: additional positional arguments to be passed to `self.call`.
      **kwargs: additional keyword arguments to be passed to `self.call`.
        **Note**: kwarg `scope` is reserved for use by the layer.

    Returns:
      Output tensor(s).

    Note:
      - If the layer's `call` method takes a `scope` keyword argument,
        this argument will be automatically set to the current variable scope.
      - If the layer's `call` method takes a `mask` argument (as some Keras
        layers do), its default value will be set to the mask generated
        for `inputs` by the previous layer (if `input` did come from
        a layer that generated a corresponding mask, i.e. if it came from
        a Keras layer with masking support.

    Raises:
      ValueError: if the layer's `call` method returns None (an invalid value).
    """
    self._set_scope(kwargs.pop('scope', None))

    in_graph_mode = context.in_graph_mode()
    # Ensure the Layer, if being reused, is working with inputs from
    # the same graph as where it was created.
    if in_graph_mode:
      try:
        ops._get_graph_from_inputs(nest.flatten(inputs), graph=self.graph)  # pylint: disable=protected-access
      except ValueError as e:
        raise ValueError('Input graph and Layer graph are not the same: %s' % e)
      user_kwargs = copy.copy(kwargs)

    # Handle Keras mask propagation from previous layer to current layer.
    previous_mask = None
    if (not hasattr(self, '_compute_previous_mask') or
        self._compute_previous_mask):
      previous_mask = _collect_previous_mask(inputs)
      if ('mask' in estimator_util.fn_args(self.call) and
         'mask' not in kwargs and
          not _is_all_none(previous_mask)):
        # The previous layer generated a mask, and mask was not explicitly pass
        # to __call__, hence we set previous_mask as the default value.
        kwargs['mask'] = previous_mask

    with vs.variable_scope(
        self._scope, reuse=(self.built or self._reuse)) as scope:
      with ops.name_scope(scope.original_name_scope):
        if not self.built:
          if not in_graph_mode:
            # Activity regularization is unsupported in Eager mode.
            if hasattr(self,
                       'activity_regularizer') and self.activity_regularizer:
              raise ValueError('activity_regularizer currently unsupported in '
                               'Eager mode. Found an activity_regularizer in '
                               '%s(%s).' % (self.__class__.__name__, self))
            # TODO(agarwal): support _keras_history in Eager mode.
            for x in _to_list(inputs):
              if hasattr(x, '_keras_history'):
                raise ValueError('_keras_history currently unsupported in '
                                 'Eager mode. Found _keras_history in %s while '
                                 'executing __call__ for %s(%s)' %
                                 (x, self.__class_.__name__, self))

          # Check input assumptions set before layer building, e.g. input rank.
          self._assert_input_compatibility(inputs)
          input_list = nest.flatten(inputs)
          if input_list and self._dtype is None:
            try:
              self._dtype = input_list[0].dtype.name
            except AttributeError:
              pass
          input_shapes = [x.get_shape() for x in input_list]What If Jesus Had Been a Republican?
You don't really believe that,do you? Why did Jesus have kids then and leave his family?
Why would he choose to die w/ a family?
Jesus came to Earth to die for our sins, not to marry and have a family. This was in prophecy from the Old Testament.
I'm familiar with the theory that Jesus had a wife, but as you must have read in your link, most Christians denounce it.
he did not "come to earth"....he was born here.
the more you try to spin this and argue the more you prove my point.
you accusing me of trying to argue about ANYTHING does not even pass the giggle test. you would argue with a stop sign.
i regret the day i interfered w/ your vendetta against your sister in law, and the focus was turned on me. you're a vindictive bitch who used to have a cabal of sycophants to help carry out your agenda.
where are they now...?
Would Jesus approve of your post?
Poor thing, he just goes off the deep end for no reason.
Cue the syncophant.
FT, are you TB's little helper now?
Did you make a list of all his derogatory terms? Of course, for you to be completely accurate, you should first check your spelling before posting.
 Same old, same old. lol.
I wonder if TB will call her out on her spelling? Hmm.
Now he'll post something with his usual unjustified air of superiority. So predictable.
She should probably be disliked at least 5 points for her spelling.
would Jesus approve of your vendetta and un