- Operating System: Windows 10
- Python Version: 3.7.11
- PyTorch Version: 1.10.1

I have two below tensors:

```
import torch
embedding_vectors = torch.tensor([
[0.01, 0.02, 0.03],
[0.07, 0.08, 0.04],
[0.05, 0.09, 0.06],
[0.51, 0.92, 0.67],
[0.55, 0.99, 0.64],
[0.17, 0.23, 0.85],
[0.45, 0.66, 0.31],
[0.01, 0.07, 0.92],
[0.25, 0.56, 0.32]
])
indices = torch.tensor([
[0, 2],
[4, 5],
[6, 0]
])
```

I want to map the **values** in `indices`

variable to **rows** in `embedding_vectors`

variable, so I expect bellow tensor as *output* :

```
[
[[0.01, 0.02, 0.03], [0.05, 0.09, 0.06]],
[[0.55, 0.99, 0.64], [0.17, 0.23, 0.85]],
[[0.45, 0.66, 0.31], [0.01, 0.02, 0.03]]
]
```

Question:

- Does PyTorch have built-in function to do this as same as
`tf.nn.embedding_lookup(embedding_vectors, indices)`

in tensorflow? - If not, how can I do this?

I used `torch.index_select(embedding_vectors , 0, indices)`

but it says that it expect a vector as indices while my `indices`

variable has 2 dimension.