Returns: The number of feature dimensions that the transformed data will have. If the transform returns an array, output_dim should be an int. If the transform returns a dict of str -> array, output_dim should be a dict str -> int, that de
(self)
| 21 | |
| 22 | @property |
| 23 | def output_dim(self) -> Union[int, Dict[str, int]]: |
| 24 | """ |
| 25 | Returns: |
| 26 | The number of feature dimensions that the transformed data will have. |
| 27 | If the transform returns an array, output_dim should be an int. |
| 28 | If the transform returns a dict of str -> array, output_dim should be a dict str -> int, that |
| 29 | described the number of features for each key in the transformed output. |
| 30 | |
| 31 | """ |
| 32 | return self._out_dim |
| 33 | |
| 34 | def transform(self, X: Dict[str, np.ndarray]) -> Any: |
| 35 | """ |
nothing calls this directly
no outgoing calls
no test coverage detected