[quote="kazum, post:6, topic:6243"]
in_indices
[/quote]
ha-haļ¼great.
can you help me review my pull request about cumsum? thanks.
https://github.com/apache/incubator-tvm/pull/5289
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[quote="adobay, post:5, topic:6243"]
```
in_indices = tf.placeholder(tf.float32, np_indices.shape, name="in_indices")
out = tf.gather_nd(in_data, indices)
```
[/quote]
These lines should be
```
in_indices = tf.placeholder(tf.int32, np_indices.shape, name="in_indices")
out = tf.gather_nd(in_data,
seems incorrect, can not pass my test case@kazum
```python
###
# GatherNd
# --
def _gather_nd(in_shape, indices):
"""test operator GatherNd"""
np_indices = np.asarray(indices, dtype='int32')
tf
https://github.com/apache/incubator-tvm/pull/5279
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I think we don't need to change the current semantics. We can easily implement
Tensorflow gather_nd with the mxnet gather_nd (and vice versa).
Here is a pseudo code:
```
tf_gather_nd(data, indices) = relay.gather_nd(data, transpose(indices, [N-1, 0,
1, ..., N-2]))
```
where N is the dimension
Currently Relay `gather_nd` op uses the [mxnet
semantic](https://mxnet.apache.org/api/python/docs/api/ndarray/op/index.html#mxnet.ndarray.op.gather_nd),
which each column in `indices` indicates the indices in `data`. However,
Tensorflow
[`gather_nd`](https://www.tensorflow.org/api_docs/python
