for base_letter in range(output_dim[3]):
cur_base=np.array(empty_onehot)
cur_base[base_letter]=1
Xtmp=np.array(np.expand_dims(X[sample_index],axis=0))
Xtmp[0][0][base_pos]=cur_base
//get the logit of Xtmp
Xtmp_logit=np.squeeze(preact_function(Xtmp),axis=0)
mutants_expanded[sample_index][0][base_pos][base_letter]=Xtmp_logit[task_index]
//subtract wt_expanded from mutants_expanded
ism_vals=mutants_expanded-wt_expanded
//For each position subtract the mean ISM score for that position from each of the 4 values
ism_vals_mean=np.expand_dims(np.mean(ism_vals,axis=3),axis=3)
After Change
wt_logit_for_task_sample=wild_type_logits[sample_index]
wt_expanded[sample_index]=np.tile(wt_logit_for_task_sample,(output_dim[2],output_dim[3]))
//mutagenize each position
temp_batch = []
tempbatch_baseposandletter = []
for base_pos in range(start_pos,end_pos):
//for each position, iterate through the 4 bases
for base_letter in range(output_dim[3]):
