Support nested layer customization and enable better 'in_channels' exception raise

[x] I've tested that my changes are compatible with the latest version of Tensorflow.
[x] I've read the Contribution Guidelines
[x] I've updated the documentation if necessary.

I briefly list the design below just for a reminder.

Nested layer customization
- Logic Each layer maintain a private attribute self._layers to store possible nested layer. When a Layer is registered as an attribute of another Layer, it is automatically added to the second layer's self._layers. Weights are collected recursively. Inner layer set to be unaware of LayerNode concept because it does not need to be involved in the model node graph. Inner layer is only regarded as the outer layer‘s 'private' attribute, which is not known by the model.
- Example
```
    class MyLayer(tl.layers.Layer):

    def __init__(self, name=None):
        super(MyLayer, self).__init__(name=name)
        self.input_layer = tl.layers.Dense(n_units=20)  # it's ok not to pass `in_channels` here
        self.build(None)
        self._built = True

    def build(self, inputs_shape=None):
        self.W = self._get_weights('weights', shape=(20, 10))

    def forward(self, inputs):
        inputs = self.input_layer(inputs)
        output = tf.matmul(inputs, self.W)
        return output
```
  MyLayer would contain three weights: W of MyLayer, W and b of the inner Dense.
'in_channels' exception in dynamic model Now it is checked when setting Layer as an attribute of dynamic Model. If in_channels not provided, there will be an exception raised, e.g.
```
The registered layer `dense_4` should be built in advance. Do you forget to pass the keyword argument 'in_channels'?
```

tensorlayer / TensorLayer