normalize_activations="expected_average_only_in",
        b_dec_init_method="geometric_median",

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
[<ipython-input-6-7c36e6ae5326>](https://localhost:8080/#) in <cell line: 0>()
     60 )
     61 # look at the next cell to see some instruction for what to do while this is running.
---> 62 sparse_autoencoder = SAETrainingRunner(cfg).run()

5 frames
[/usr/local/lib/python3.11/dist-packages/sae_lens/sae_training_runner.py](https://localhost:8080/#) in __init__(self, cfg, override_dataset, override_model, override_sae)
     83                     )
     84                 )
---> 85                 self._init_sae_group_b_decs()
     86         else:
     87             self.sae = override_sae

[/usr/local/lib/python3.11/dist-packages/sae_lens/sae_training_runner.py](https://localhost:8080/#) in _init_sae_group_b_decs(self)
    169 
    170         if self.cfg.b_dec_init_method == "geometric_median":
--> 171             layer_acts = self.activations_store.storage_buffer.detach()[:, 0, :]
    172             # get geometric median of the activations if we're using those.
    173             median = compute_geometric_median(

[/usr/local/lib/python3.11/dist-packages/sae_lens/training/activations_store.py](https://localhost:8080/#) in storage_buffer(self)
    476         if self._storage_buffer is None:
    477             self._storage_buffer = _filter_buffer_acts(
--> 478                 self.get_buffer(self.half_buffer_size), self.exclude_special_tokens
    479             )
    480 

[/usr/local/lib/python3.11/dist-packages/torch/utils/_contextlib.py](https://localhost:8080/#) in decorate_context(*args, **kwargs)
    114     def decorate_context(*args, **kwargs):
    115         with ctx_factory():
--> 116             return func(*args, **kwargs)
    117 
    118     return decorate_context

[/usr/local/lib/python3.11/dist-packages/sae_lens/training/activations_store.py](https://localhost:8080/#) in get_buffer(self, n_batches_in_buffer, raise_on_epoch_end, shuffle)
    707         # every buffer should be normalized:
    708         if self.normalize_activations == "expected_average_only_in":
--> 709             new_buffer_activations = self.apply_norm_scaling_factor(
    710                 new_buffer_activations
    711             )

[/usr/local/lib/python3.11/dist-packages/sae_lens/training/activations_store.py](https://localhost:8080/#) in apply_norm_scaling_factor(self, activations)
    423     def apply_norm_scaling_factor(self, activations: torch.Tensor) -> torch.Tensor:
    424         if self.estimated_norm_scaling_factor is None:
--> 425             raise ValueError(
    426                 "estimated_norm_scaling_factor is not set, call set_norm_scaling_factor_if_needed() first"
    427             )

ValueError: estimated_norm_scaling_factor is not set, call set_norm_scaling_factor_if_needed() first