Sample with a given seed. This function will generates a indexer based on your seed. Args: num_of_fetches (int): [description] num_of_elements (int): [description] seed (int, optional): [description]. Defaults to 0x20211230. Returns: torch.Tensor: [descr
(
num_of_fetches: int,
num_of_elements: int,
seed: int = 0x20211230)
| 2 | |
| 3 | |
| 4 | def generate_indexer( |
| 5 | num_of_fetches: int, |
| 6 | num_of_elements: int, |
| 7 | seed: int = 0x20211230) -> torch.Tensor: |
| 8 | """Sample with a given seed. This function will generates a indexer based |
| 9 | on your seed. |
| 10 | |
| 11 | Args: |
| 12 | num_of_fetches (int): [description] |
| 13 | num_of_elements (int): [description] |
| 14 | seed (int, optional): [description]. Defaults to 0x20211230. |
| 15 | |
| 16 | Returns: |
| 17 | torch.Tensor: [description] |
| 18 | """ |
| 19 | |
| 20 | indexer = [] |
| 21 | for i in range(num_of_fetches): |
| 22 | indexer.append(seed % num_of_elements) |
| 23 | seed = (0x343FD * seed + 0x269EC3) % (2 << 31) |
| 24 | return torch.tensor(indexer, dtype=torch.int32) |
| 25 | |
| 26 | def generate_torch_indexer( |
| 27 | num_of_fetches: int, |
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