Tensorflow equivalent to torch.Tensor.index_copy
I am implementing the tensorflow equivalent of the model here originally implemented using pytorch. Everything was going smoothly until I encountered this particular line of code.
batch_current = Variable(torch.zeros(size, self.embedding_dim))

# self.embedding and self.W_c are pytorch network layers I have created
batch_current = self.W_c(batch_current.index_copy(0, Variable(torch.LongTensor(index)),
                                                         self.embedding(Variable(self.th.LongTensor(current_node)))))

If search for the documentation of index_copy and it seems all it does is to copy a group of elements at a certain index and on a common axis and assign it to another tensor. But I don't really want to write some buggy code, so before attempting any self-implementation, I wish to know if you folks have an idea of how I can go about implementing it.
The model is from this paper and yes, I have searched other tensorflow implementations, but they don't seem to make so much sense to me.

What you need is the tf.tensor_scatter_nd_update in tensorflow to get equivalent operation like Tensor.index_copy_ of pytorch. Here is one demonstration shown below.
In pytorch, you have
import torch 

tensor = torch.zeros(5, 3)
indices = torch.tensor([0, 4, 2])
updates= torch.tensor([[1, 2, 3], 
                       [4, 5, 6], 
                       [7, 8, 9]], dtype=torch.float)
tensor.index_copy_(0, indices, updates)

tensor([[1., 2., 3.],
        [0., 0., 0.],
        [7., 8., 9.],
        [0., 0., 0.],
        [4., 5., 6.]])

And in tensorflow, you can do
import tensorflow as tf

tensor = tf.zeros([5,3])
indices = tf.constant([[0], [4], [2]])
updates  = tf.constant([[1, 2, 3], 
                        [4, 5, 6], 
                        [7, 8, 9]], dtype=tf.float32)
tensor = tf.tensor_scatter_nd_update(tensor, indices, updates)
tensor.numpy()
array([[1., 2., 3.],
       [0., 0., 0.],
       [7., 8., 9.],
       [0., 0., 0.],
       [4., 5., 6.]], dtype=float32)

