Hello,

I am trying to figure out the variance (uncertainty) estimation in regression tasks. I have looked into https://github.com/deepchem/deepchem/blob/master/examples/tutorials/25_Uncertainty_In_Deep_Learning.ipynb notebook and referred to the graph_models.py script. I want to know if the three lines below in graph_models.py from line number 830 compute both aleatoric and epistemic uncertainty?

`self.uncertainty_dense = Dense(n_tasks)`

`self.uncertainty_trim = TrimGraphOutput()`

`self.uncertainty_activation = Activation(tf.exp)`

compute the variance? How is the line

`self.uncertainty_dense = Dense(n_tasks)`

differ from line 828 which I believe is the predicted output

`self.regression_dense = Dense(n_tasks)`

Would really appreciate a response.

Thank you!