diff --git "a/metrics.log" "b/metrics.log"
new file mode 100644--- /dev/null
+++ "b/metrics.log"
@@ -0,0 +1,5615 @@
+==========================================
+SLURM_JOB_ID = 721829
+SLURM_JOB_NODELIST = gpu003
+==========================================
+### Running valid_mopoe ###
+W&B disabled.
+Downloading config file ...
+Downloading JNFDcca files for rebuilding...
+['dcca_networks', 'decoders']
+
Downloading dcca_networks.pkl:   0%|          | 0.00/9.00M [00:00<?, ?B/s]
Downloading dcca_networks.pkl: 100%|██████████| 9.00M/9.00M [00:00<00:00, 10.8MB/s]
Downloading dcca_networks.pkl: 100%|██████████| 9.00M/9.00M [00:00<00:00, 10.8MB/s]
+Successfully downloaded dcca_networks architecture.
+
Downloading decoders.pkl:   0%|          | 0.00/11.5M [00:00<?, ?B/s]
Downloading decoders.pkl:  91%|█████████▏| 10.5M/11.5M [00:00<00:00, 12.1MB/s]
Downloading decoders.pkl: 100%|██████████| 11.5M/11.5M [00:00<00:00, 12.8MB/s]
+Successfully downloaded decoders architecture.
+Successfully downloaded JNFDcca model!
+Subset ['m0'] accuracies 
+{'m1': 0.6294, 'm2': 0.5826, 'm3': 0.6001, 'm4': 0.427}
+Mean subset ['m0'] accuracies : 0.559775
+Subset ['m1'] accuracies 
+{'m0': 0.4785, 'm2': 0.6687, 'm3': 0.6864, 'm4': 0.4771}
+Mean subset ['m1'] accuracies : 0.577675
+Subset ['m2'] accuracies 
+{'m0': 0.475, 'm1': 0.7235, 'm3': 0.6725, 'm4': 0.4633}
+Mean subset ['m2'] accuracies : 0.583575
+Subset ['m3'] accuracies 
+{'m0': 0.4686, 'm1': 0.6968, 'm2': 0.6444, 'm4': 0.4628}
+Mean subset ['m3'] accuracies : 0.56815
+Subset ['m4'] accuracies 
+{'m0': 0.4363, 'm1': 0.6566, 'm2': 0.6141, 'm3': 0.6259}
+Mean subset ['m4'] accuracies : 0.583225
+/home/asenella/miniconda3/envs/multivaenv/lib/python3.10/site-packages/torch/_tensor.py:775: UserWarning: non-inplace resize is deprecated
+  warnings.warn("non-inplace resize is deprecated")
+Subset ['m0', 'm1'] accuracies 
+{'m2': 0.7621, 'm3': 0.7977, 'm4': 0.5634}
+Mean subset ['m0', 'm1'] accuracies : 0.7077333333333334
+Subset ['m0', 'm2'] accuracies 
+{'m1': 0.8245, 'm3': 0.7932, 'm4': 0.578}
+Mean subset ['m0', 'm2'] accuracies : 0.7319
+Subset ['m0', 'm3'] accuracies 
+{'m1': 0.8136, 'm2': 0.7553, 'm4': 0.5667}
+Mean subset ['m0', 'm3'] accuracies : 0.7118666666666668
+Subset ['m0', 'm4'] accuracies 
+{'m1': 0.7871, 'm2': 0.7226, 'm3': 0.7663}
+Mean subset ['m0', 'm4'] accuracies : 0.7586666666666666
+Subset ['m1', 'm2'] accuracies 
+{'m0': 0.5551, 'm3': 0.8283, 'm4': 0.576}
+Mean subset ['m1', 'm2'] accuracies : 0.6531333333333333
+Subset ['m1', 'm3'] accuracies 
+{'m0': 0.5559, 'm2': 0.786, 'm4': 0.5633}
+Mean subset ['m1', 'm3'] accuracies : 0.6350666666666666
+Subset ['m1', 'm4'] accuracies 
+{'m0': 0.5502, 'm2': 0.7675, 'm3': 0.8133}
+Mean subset ['m1', 'm4'] accuracies : 0.7103333333333333
+Subset ['m2', 'm3'] accuracies 
+{'m0': 0.5637, 'm1': 0.8423, 'm4': 0.5739}
+Mean subset ['m2', 'm3'] accuracies : 0.6599666666666667
+Subset ['m2', 'm4'] accuracies 
+{'m0': 0.5585, 'm1': 0.824, 'm3': 0.8116}
+Mean subset ['m2', 'm4'] accuracies : 0.7313666666666666
+Subset ['m3', 'm4'] accuracies 
+{'m0': 0.5479, 'm1': 0.8175, 'm2': 0.7578}
+Mean subset ['m3', 'm4'] accuracies : 0.7077333333333334
+Subset ['m0', 'm1', 'm2'] accuracies 
+{'m3': 0.8821, 'm4': 0.6031}
+Mean subset ['m0', 'm1', 'm2'] accuracies : 0.7425999999999999
+Subset ['m0', 'm1', 'm3'] accuracies 
+{'m2': 0.8177, 'm4': 0.6044}
+Mean subset ['m0', 'm1', 'm3'] accuracies : 0.71105
+Subset ['m0', 'm1', 'm4'] accuracies 
+{'m2': 0.8085, 'm3': 0.8683}
+Mean subset ['m0', 'm1', 'm4'] accuracies : 0.8384
+Subset ['m0', 'm2', 'm3'] accuracies 
+{'m1': 0.8935, 'm4': 0.6131}
+Mean subset ['m0', 'm2', 'm3'] accuracies : 0.7533
+Subset ['m0', 'm2', 'm4'] accuracies 
+{'m1': 0.8756, 'm3': 0.8691}
+Mean subset ['m0', 'm2', 'm4'] accuracies : 0.87235
+Subset ['m0', 'm3', 'm4'] accuracies 
+{'m1': 0.8704, 'm2': 0.8053}
+Mean subset ['m0', 'm3', 'm4'] accuracies : 0.83785
+Subset ['m1', 'm2', 'm3'] accuracies 
+{'m0': 0.5897, 'm4': 0.6009}
+Mean subset ['m1', 'm2', 'm3'] accuracies : 0.5952999999999999
+Subset ['m1', 'm2', 'm4'] accuracies 
+{'m0': 0.5734, 'm3': 0.888}
+Mean subset ['m1', 'm2', 'm4'] accuracies : 0.7307
+Subset ['m1', 'm3', 'm4'] accuracies 
+{'m0': 0.5809, 'm2': 0.818}
+Mean subset ['m1', 'm3', 'm4'] accuracies : 0.6994499999999999
+Subset ['m2', 'm3', 'm4'] accuracies 
+{'m0': 0.5923, 'm1': 0.8911}
+Mean subset ['m2', 'm3', 'm4'] accuracies : 0.7417
+Subset ['m0', 'm1', 'm2', 'm3'] accuracies 
+{'m4': 0.6127}
+Mean subset ['m0', 'm1', 'm2', 'm3'] accuracies : 0.6127
+Subset ['m0', 'm1', 'm2', 'm4'] accuracies 
+{'m3': 0.9173}
+Mean subset ['m0', 'm1', 'm2', 'm4'] accuracies : 0.9173
+Subset ['m0', 'm1', 'm3', 'm4'] accuracies 
+{'m2': 0.8293}
+Mean subset ['m0', 'm1', 'm3', 'm4'] accuracies : 0.8293
+Subset ['m0', 'm2', 'm3', 'm4'] accuracies 
+{'m1': 0.918}
+Mean subset ['m0', 'm2', 'm3', 'm4'] accuracies : 0.918
+Subset ['m1', 'm2', 'm3', 'm4'] accuracies 
+{'m0': 0.585}
+Mean subset ['m1', 'm2', 'm3', 'm4'] accuracies : 0.585
+Conditional accuracies for 1 modalities : 0.5744800000000001 +- 0.009227220599942305
+Conditional accuracies for 2 modalities : 0.7007766666666667 +- 0.037175150152876174
+Conditional accuracies for 3 modalities : 0.75227 +- 0.07678652290604128
+Conditional accuracies for 4 modalities : 0.77246 +- 0.1456405245802143
+Joint coherence : 0.029500000178813934
+starting to sample from poe_subset, divide prior =  True
+tensor([[1.0000],
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+        [0.9900],
+        [0.9700],
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+        [0.9800],
+        [0.9900],
+        [1.0000],
+        [0.9900],
+        [0.9500]], device='cuda:0')
+starting to sample from poe_subset, divide prior =  True
+tensor([[1.0000],
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+        [0.9700],
+        [0.9900],
+        [0.9900],
+        [0.9700],
+        [0.9800],
+        [0.9700],
+        [0.9900],
+        [1.0000]], device='cuda:0')
+starting to sample from poe_subset, divide prior =  True
+tensor([[0.9900],
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+        [1.0000],
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+        [0.9600],
+        [0.9600],
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+        [0.9800]], device='cuda:0')
+starting to sample from poe_subset, divide prior =  True
+tensor([[1.0000],
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+        [0.9800],
+        [0.9600],
+        [1.0000],
+        [1.0000],
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+starting to sample from poe_subset, divide prior =  True
+tensor([[0.9800],
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+starting to sample from poe_subset, divide prior =  True
+tensor([[0.9700],
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+        [0.9800],
+        [1.0000],
+        [0.9900],
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+        [0.9900]], device='cuda:0')
+starting to sample from poe_subset, divide prior =  True
+tensor([[1.0000],
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+        [0.9900],
+        [0.9800],
+        [1.0000],
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+starting to sample from poe_subset, divide prior =  True
+tensor([[0.9600],
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+        [0.9500],
+        [0.9900],
+        [0.9900],
+        [1.0000],
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+        [0.9300]], device='cuda:0')
+starting to sample from poe_subset, divide prior =  True
+tensor([[0.9900],
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+        [0.9900],
+        [0.9900],
+        [0.9700],
+        [0.9900],
+        [0.9700],
+        [0.9900],
+        [1.0000],
+        [1.0000]], device='cuda:0')
+starting to sample from poe_subset, divide prior =  True
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+        [0.9700],
+        [0.9800],
+        [0.0000],
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+        [1.0000]], device='cuda:0')
+starting to sample from poe_subset, divide prior =  True
+tensor([[0.9900],
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+        [0.9900],
+        [0.9900],
+        [1.0000],
+        [1.0000],
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+starting to sample from poe_subset, divide prior =  True
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+starting to sample from poe_subset, divide prior =  True
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+        [0.9900],
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+        [1.0000]], device='cuda:0')
+starting to sample from poe_subset, divide prior =  True
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+starting to sample from poe_subset, divide prior =  True
+tensor([[1.0000],
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+        [0.9900],
+        [0.9700],
+        [0.9900],
+        [0.9900],
+        [0.9800],
+        [0.9600],
+        [0.9800],
+        [0.9900]], device='cuda:0')
+starting to sample from poe_subset, divide prior =  True
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+        [1.0000]], device='cuda:0')
+starting to sample from poe_subset, divide prior =  True
+tensor([[0.9900],
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+starting to sample from poe_subset, divide prior =  True
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+starting to sample from poe_subset, divide prior =  True
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+starting to sample from poe_subset, divide prior =  True
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+starting to sample from poe_subset, divide prior =  True
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+        [0.9800],
+        [0.9800],
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+        [1.0000]], device='cuda:0')
+starting to sample from poe_subset, divide prior =  True
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+        [0.9800],
+        [0.9700],
+        [1.0000],
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+        [0.9700],
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+        [0.9900]], device='cuda:0')
+starting to sample from poe_subset, divide prior =  True
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+        [0.9900],
+        [0.9900],
+        [1.0000],
+        [1.0000],
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+        [1.0000],
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+starting to sample from poe_subset, divide prior =  True
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+        [0.9900],
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+        [0.9800],
+        [0.9900],
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+        [0.9800],
+        [0.9900],
+        [1.0000],
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+starting to sample from poe_subset, divide prior =  True
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+Traceback (most recent call last):
+  File "/home/asenella/dev/multivae_package/multimodal_vaes/examples/mmnist/validate/script_hf.py", line 34, in <module>
+    operations=[CommitOperationAdd(path_or_fileobj=tmp_output + '/metrics.log')])
+TypeError: CommitOperationAdd.__init__() missing 1 required positional argument: 'path_in_repo'