message: Add code example for tf.keras.layers.AveragePooling1D.
diff: @@ -204,6 +204,73 @@ class MaxPooling1D(Pooling1D):
  class AveragePooling1D(Pooling1D):
  """Average pooling for temporal data.
  + Downsamples the input representation by taking the average value over the
  + window defined by `pool_size`. The window is shifted by `strides`. The
  + resulting output when using "valid" padding option has a shape of:
  + `output_shape = (input_shape - pool_size + 1) / strides)`
  +
  + The resulting output shape when using the "same" padding option is:
  + `output_shape = input_shape / strides`
  +
  + For example, for strides=1 and padding="valid":
  +
  + >>> x = tf.constant([1., 2., 3., 4., 5.])
  + >>> x = tf.reshape(x, [1, 5, 1])
  + >>> x
  + <tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
  + array([[[1.],
  + [2.],
  + [3.],
  + [4.],
  + [5.]], dtype=float32)>
  + >>> avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2,
  + ... strides=1, padding='valid')
  + >>> avg_pool_1d(x)
  + <tf.Tensor: shape=(1, 4, 1), dtype=float32, numpy=
  + array([[[1.5],
  + [2.5],
  + [3.5],
  + [4.5]]], dtype=float32)>
  +
  + For example, for strides=2 and padding="valid":
  +
  + >>> x = tf.constant([1., 2., 3., 4., 5.])
  + >>> x = tf.reshape(x, [1, 5, 1])
  + >>> x
  + <tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
  + array([[[1.],
  + [2.],
  + [3.],
  + [4.],
  + [5.]], dtype=float32)>
  + >>> avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2,
  + ... strides=2, padding='valid')
  + >>> avg_pool_1d(x)
  + <tf.Tensor: shape=(1, 2, 1), dtype=float32, numpy=
  + array([[[1.5],
  + [3.5]]], dtype=float32)>
  +
  + For example, for strides=1 and padding="same":
  +
  + >>> x = tf.constant([1., 2., 3., 4., 5.])
  + >>> x = tf.reshape(x, [1, 5, 1])
  + >>> x
  + <tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
  + array([[[1.],
  + [2.],
  + [3.],
  + [4.],
  + [5.]], dtype=float32)>
  + >>> avg_pool_1d = tf.keras.layers.AveragePooling1D(pool_size=2,
  + ... strides=1, padding='same')
  + >>> avg_pool_1d(x)
  + <tf.Tensor: shape=(1, 5, 1), dtype=float32, numpy=
  + array([[[1.5],
  + [2.5],
  + [3.5],
  + [4.5],
  + [5.]]], dtype=float32)>
  +
  Args:
  pool_size: Integer, size of the average pooling windows.
  strides: Integer, or None. Factor by which to downscale.
  