---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
     17 # TypeError here:
     18 explainer = shap.Explainer(model.predict, masker=background)
---> 20 shap_values = explainer(X_test)
     21 exp = shap.Explanation(shap_values.values, shap_values.base_values, shap_values.data)

File ~/miniconda3/lib/python3.9/site-packages/shap/explainers/_permutation.py:76, in Permutation.__call__(self, max_evals, main_effects, error_bounds, batch_size, outputs, silent, *args)
     72 def __call__(self, *args, max_evals=500, main_effects=False, error_bounds=False, batch_size="auto",
     73              outputs=None, silent=False):
     74     """ Explain the output of the model on the given arguments.
     75     """
---> 76     return super().__call__(
     77         *args, max_evals=max_evals, main_effects=main_effects, error_bounds=error_bounds, batch_size=batch_size,
     78         outputs=outputs, silent=silent
     79     )

File ~/miniconda3/lib/python3.9/site-packages/shap/explainers/_explainer.py:264, in Explainer.__call__(self, max_evals, main_effects, error_bounds, batch_size, outputs, silent, *args, **kwargs)
    262     feature_names = [[] for _ in range(len(args))]
    263 for row_args in show_progress(zip(*args), num_rows, self.__class__.__name__+" explainer", silent):
--> 264     row_result = self.explain_row(
    265         *row_args, max_evals=max_evals, main_effects=main_effects, error_bounds=error_bounds,
    266         batch_size=batch_size, outputs=outputs, silent=silent, **kwargs
    267     )
    268     values.append(row_result.get("values", None))
    269     output_indices.append(row_result.get("output_indices", None))

File ~/miniconda3/lib/python3.9/site-packages/shap/explainers/_permutation.py:134, in Permutation.explain_row(self, max_evals, main_effects, error_bounds, batch_size, outputs, silent, *row_args)
    131     i += 1
    133 # evaluate the masked model
--> 134 outputs = fm(masks, zero_index=0, batch_size=batch_size)
    136 if row_values is None:
    137     row_values = np.zeros((len(fm),) + outputs.shape[1:])

File ~/miniconda3/lib/python3.9/site-packages/shap/utils/_masked_model.py:66, in MaskedModel.__call__(self, masks, zero_index, batch_size)
     64         full_masks = np.zeros((int(np.sum(masks >= 0)), self._masker_cols), dtype=bool)
     65         _convert_delta_mask_to_full(masks, full_masks)
---> 66         return self._full_masking_call(full_masks, zero_index=zero_index, batch_size=batch_size)
     68 else:
     69     return self._full_masking_call(masks, batch_size=batch_size)

File ~/miniconda3/lib/python3.9/site-packages/shap/utils/_masked_model.py:106, in MaskedModel._full_masking_call(self, masks, zero_index, batch_size)
    103     masked_inputs = (masked_inputs,)
    105 # masked_inputs = self.masker(mask, *self.args)
--> 106 num_mask_samples[i] = len(masked_inputs[0])
    108 # see which rows have been updated, so we can only evaluate the model on the rows we need to
    109 if i == 0 or self._variants is None:

TypeError: object of type 'NoneType' has no len()