message: Improve docstring of ConvLSTM2D layer.
diff: @@ -698,10 +698,16 @@ class ConvLSTM2DCell(DropoutRNNCellMixin, Layer):
  @keras_export('keras.layers.ConvLSTM2D')
  class ConvLSTM2D(ConvRNN2D):
  - """Convolutional LSTM.
  + """2D Convolutional LSTM layer.
  - It is similar to an LSTM layer, but the input transformations
  - and recurrent transformations are both convolutional.
  + A convolutional LSTM is similar to an LSTM, but the input transformations
  + and recurrent transformations are both convolutional. This layer is typically
  + used to process timeseries of images (i.e. video-like data).
  +
  + It is known to perform well for weather data forecasting,
  + using inputs that are timeseries of 2D grids of sensor values.
  + It isn't usually applied to regular video data, due to its high computational
  + cost.
  Args:
  filters: Integer, the dimensionality of the output space
  @@ -775,7 +781,7 @@ class ConvLSTM2D(ConvRNN2D):
  the linear transformation of the recurrent state.
  Call arguments:
  - inputs: A 5D tensor.
  + inputs: A 5D float tensor (see input shape description below).
  mask: Binary tensor of shape `(samples, timesteps)` indicating whether
  a given timestep should be masked.
  training: Python boolean indicating whether the layer should behave in
  @@ -823,6 +829,20 @@ class ConvLSTM2D(ConvRNN2D):
  - [Shi et al., 2015](http://arxiv.org/abs/1506.04214v1)
  (the current implementation does not include the feedback loop on the
  cells output).
  +
  + Example:
  +
  + ```python
  + steps = 10
  + height = 32
  + width = 32
  + input_channels = 3
  + output_channels = 6
  +
  + inputs = tf.keras.Input(shape=(steps, height, width, input_channels))
  + layer = tf.keras.layers.ConvLSTM2D(filters=output_channels, kernel_size=3)
  + outputs = layer(inputs)
  + ```
  """
  def __init__(self,
  