(input_tensor, data)
| 837 | X_adaption_needed = torch.randn(input_shape) |
| 838 | |
| 839 | def logits_adaptor(input_tensor, data): return torch.flatten( |
| 840 | input_tensor, start_dim=1) |
| 841 | |
| 842 | predictions = modAL.dropout.get_predictions( |
| 843 | learner, X_adaption_needed, dropout_layer_indexes=[], |
nothing calls this directly
no outgoing calls
no test coverage detected