输入：
  input = np.array([[1, 1], [2, 2], [3, 3]])
  index = np.array([2, 1, 0, 1])
  # updates的维度需要和input一样
  # updates 维度 > 1 的shape要和input一样
  updates = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
  overwrite = False

计算过程：
  if not overwrite:
     for i in range(len(index)):
       input[index[i]] = np.zeros((2))

  # 根据index中的索引值取updates中的数据更新到input中去
  for i in range(len(index)):
    if (overwirte):
      input[index[i]] = updates[i]
    else:
      input[index[i]] += updates[i]

输出：
  out # np.array([[3, 3], [6, 6], [1, 1]])
  out.shape # [3, 2]

import numpy as np
import paddle.fluid as fluid

input = fluid.layers.data(name='data', shape=[3, 2], dtype='float32', append_batch_size=False)
index = fluid.layers.data(name='index', shape=[4], dtype='int64', append_batch_size=False)
updates = fluid.layers.data(name='update', shape=[4, 2], dtype='float32', append_batch_size=False)

output = fluid.layers.scatter(input, index, updates, overwrite=False)

exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())

in_data = np.array([[1, 1], [2, 2], [3, 3]]).astype(np.float32)
index_data = np.array([2, 1, 0, 1]).astype(np.int64)
update_data = np.array([[1, 1], [2, 2], [3, 3], [4, 4]]).astype(np.float32)

res = exe.run(fluid.default_main_program(), feed={'data':in_data, "index":index_data, "update":update_data}, fetch_list=[output])
print(res)
# [array([[3., 3.],
#   [6., 6.],
#   [1., 1.]], dtype=float32)]