Scatter updates into an existing tensor according to indices.

This operation creates a new tensor by applying sparse updates to the passed in tensor. This operation is very similar to tf.scatter_nd, except that the updates are scattered onto an existing tensor (as opposed to a zero-tensor). If the memory for the existing tensor cannot be re-used, a copy is made and updated.

If indices contains duplicates, then their updates are accumulated (summed).

indices is an integer tensor containing indices into a new tensor of shape shape. The last dimension of indices can be at most the rank of shape:

indices.shape[-1] <= shape.rank

The last dimension of indices corresponds to indices into elements (if indices.shape[-1] = shape.rank) or slices (if indices.shape[-1] < shape.rank) along dimension indices.shape[-1] of shape. updates is a tensor with shape

indices.shape[:-1] + shape[indices.shape[-1]:]

The simplest form of scatter is to insert individual elements in a tensor by index. For example, say we want to insert 4 scattered elements in a rank-1 tensor with 8 elements.

In Python, this scatter operation would look like this:

    indices = tf.constant([[4], [3], [1], [7]])
    updates = tf.constant([9, 10, 11, 12])
    tensor = tf.ones([8], dtype=tf.int32)
    print(tf.tensor_scatter_nd_update(tensor, indices, updates))
        tf.Tensor([ 1 11  1 10  9  1  1 12], shape=(8,), dtype=int32)

We can also, insert entire slices of a higher rank tensor all at once. For example, if we wanted