message: Add code example for tf.keras.layers.AveragePooling2D.
diff: @@ -534,6 +534,64 @@ class MaxPooling2D(Pooling2D):
  class AveragePooling2D(Pooling2D):
  """Average pooling operation for spatial data.
  + Downsamples the input representation by taking the average value over the
  + window defined by `pool_size` for each dimension along the features axis.
  + The window is shifted by `strides` in each dimension. The resulting output
  + when using "valid" padding option has a shape(number of rows or columns) of:
  + `output_shape = math.floor((input_shape - pool_size) / strides) + 1`
  + (when input_shape >= pool_size)
  +
  + The resulting output shape when using the "same" padding option is:
  + `output_shape = math.floor((input_shape - 1) / strides) + 1`
  +
  + For example, for stride=(1,1) and padding="valid":
  +
  + >>> x = tf.constant([[1., 2., 3.],
  + ... [4., 5., 6.],
  + ... [7., 8., 9.]])
  + >>> x = tf.reshape(x, [1, 3, 3, 1])
  + >>> avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(2, 2),
  + ... strides=(1, 1), padding='valid')
  + >>> avg_pool_2d(x)
  + <tf.Tensor: shape=(1, 2, 2, 1), dtype=float32, numpy=
  + array([[[[3.],
  + [4.]],
  + [[6.],
  + [7.]]]], dtype=float32)>
  +
  + For example, for stride=(2,2) and padding="valid":
  +
  + >>> x = tf.constant([[1., 2., 3., 4.],
  + ... [5., 6., 7., 8.],
  + ... [9., 10., 11., 12.]])
  + >>> x = tf.reshape(x, [1, 3, 4, 1])
  + >>> avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(2, 2),
  + ... strides=(2, 2), padding='valid')
  + >>> avg_pool_2d(x)
  + <tf.Tensor: shape=(1, 1, 2, 1), dtype=float32, numpy=
  + array([[[[3.5],
  + [5.5]]]], dtype=float32)>
  +
  + For example, for stride=(1,1) and padding="same":
  +
  + >>> x = tf.constant([[1., 2., 3.],
  + ... [4., 5., 6.],
  + ... [7., 8., 9.]])
  + >>> x = tf.reshape(x, [1, 3, 3, 1])
  + >>> avg_pool_2d = tf.keras.layers.AveragePooling2D(pool_size=(2, 2),
  + ... strides=(1, 1), padding='same')
  + >>> avg_pool_2d(x)
  + <tf.Tensor: shape=(1, 3, 3, 1), dtype=float32, numpy=
  + array([[[[3.],
  + [4.],
  + [4.5]],
  + [[6.],
  + [7.],
  + [7.5]],
  + [[7.5],
  + [8.5],
  + [9.]]]], dtype=float32)>
  +
  Args:
  pool_size: integer or tuple of 2 integers,
  factors by which to downscale (vertical, horizontal).
  