diff --git a/neural_processes/models/lstm_seqseq.py b/neural_processes/models/lstm_seqseq.py
index d6ad0e8..3472cbd 100644
--- a/neural_processes/models/lstm_seqseq.py
+++ b/neural_processes/models/lstm_seqseq.py
@@ -8,7 +8,11 @@ from torch.nn import functional as F
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
 from test_tube import Experiment, HyperOptArgumentParser
-from neural_processes.data.smart_meter import collate_fns, SmartMeterDataSet, get_smartmeter_df
+from neural_processes.data.smart_meter import (
+    collate_fns,
+    SmartMeterDataSet,
+    get_smartmeter_df,
+)
 import torchvision.transforms as transforms
 from neural_processes.plot import plot_from_loader_to_tensor, plot_from_loader
 from argparse import ArgumentParser
@@ -20,20 +24,23 @@ import torch
 import io
 import PIL
 from torchvision.transforms import ToTensor
+
 from neural_processes.modules import BatchNormSequence
 from neural_processes.data.smart_meter import get_smartmeter_df
 
 from neural_processes.utils import ObjectDict
 from neural_processes.lightning import PL_Seq2Seq
+from ..logger import logger
+from ..utils import hparams_power
 
 
 class Seq2SeqNet(nn.Module):
-    def __init__(self, hparams, _min_std = 0.05):
+    def __init__(self, hparams, _min_std=0.05):
         super().__init__()
+        hparams = hparams_power(hparams)
         self.hparams = hparams
         self._min_std = _min_std
 
-
         self.norm_input = BatchNormSequence(self.hparams.input_size)
         self.encoder = nn.LSTM(
             input_size=self.hparams.input_size,
@@ -43,7 +50,9 @@ class Seq2SeqNet(nn.Module):
             bidirectional=self.hparams.bidirectional,
             dropout=self.hparams.lstm_dropout,
         )
-        self.multihead_attn = nn.MultiheadAttention(self.hparams.hidden_size, num_heads=8)
+        self.multihead_attn = nn.MultiheadAttention(
+            self.hparams.hidden_size, num_heads=8
+        )
 
         self.norm_target = BatchNormSequence(self.hparams.input_size_decoder)
         self.decoder = nn.LSTM(
@@ -54,9 +63,8 @@ class Seq2SeqNet(nn.Module):
             bidirectional=self.hparams.bidirectional,
             dropout=self.hparams.lstm_dropout,
         )
-        self.hidden_out_size = (
-            self.hparams.hidden_size
-            * (self.hparams.bidirectional + 1)
+        self.hidden_out_size = self.hparams.hidden_size * (
+            self.hparams.bidirectional + 1
         )
         self.mean = nn.Linear(self.hidden_out_size, self.hparams.output_size)
         self.std = nn.Linear(self.hidden_out_size, self.hparams.output_size)
@@ -76,74 +84,86 @@ class Seq2SeqNet(nn.Module):
         # context_x, d_encoded, target_x = k, v, q
 
         # query, key, value = target_x, context_x, d_encoded
-        attn_output, _ = self.multihead_attn(h_out.permute(1, 0, 2), h_out.permute(1, 0, 2), h_out.permute(1, 0, 2))
+        attn_output, _ = self.multihead_attn(
+            h_out.permute(1, 0, 2), h_out.permute(1, 0, 2), h_out.permute(1, 0, 2)
+        )
         h_out = attn_output.permute(1, 0, 2).contiguous()
-        attn_output, _ = self.multihead_attn(cell.permute(1, 0, 2), cell.permute(1, 0, 2), cell.permute(1, 0, 2))
+        attn_output, _ = self.multihead_attn(
+            cell.permute(1, 0, 2), cell.permute(1, 0, 2), cell.permute(1, 0, 2)
+        )
         cell = attn_output.permute(1, 0, 2).contiguous()
 
         outputs, (_, _) = self.decoder(target_x, (h_out, cell))
         # output = [batch size, seq len, hid dim * n directions]
-        
+
         # outputs: [B, T, num_direction * H]
         mean = self.mean(outputs)
         log_sigma = self.std(outputs)
         if self._use_lvar:
-            log_sigma = torch.clamp(log_sigma, math.log(self._min_std), -math.log(self._min_std))
+            log_sigma = torch.clamp(
+                log_sigma, math.log(self._min_std), -math.log(self._min_std)
+            )
             sigma = torch.exp(log_sigma)
         else:
             sigma = self._min_std + (1 - self._min_std) * F.softplus(log_sigma)
-        y_dist=torch.distributions.Normal(mean, sigma)
-        
+        y_dist = torch.distributions.Normal(mean, sigma)
+
         # Loss
         loss_mse = loss_p = None
         if target_y is not None:
-            loss_mse = F.mse_loss(mean, target_y, reduction='none')
+            loss_mse = F.mse_loss(mean, target_y, reduction="none")
             if self._use_lvar:
                 loss_p = -log_prob_sigma(target_y, mean, log_sigma)
             else:
                 loss_p = -y_dist.log_prob(target_y).mean(-1)
-            
+
             if self.hparams["context_in_target"]:
-                loss_p[:context_x.size(1)] /= 100
-                loss_mse[:context_x.size(1)] /= 100
+                loss_p[: context_x.size(1)] /= 100
+                loss_mse[: context_x.size(1)] /= 100
             # # Don't catch loss on context window
             # mean = mean[:, self.hparams.num_context:]
             # log_sigma = log_sigma[:, self.hparams.num_context:]
 
         y_pred = y_dist.rsample if self.training else y_dist.loc
-        return y_pred, dict(loss_p=loss_p.mean(), loss_mse=loss_mse.mean()), dict(log_sigma=log_sigma, dist=y_dist)
+        return (
+            y_pred,
+            dict(loss_p=loss_p.mean(), loss_mse=loss_mse.mean()),
+            dict(log_sigma=log_sigma, dist=y_dist),
+        )
 
 
 class LSTMSeq2Seq_PL(PL_Seq2Seq):
-    def __init__(self, hparams,
-        MODEL_CLS=Seq2SeqNet, **kwargs):
-        super().__init__(hparams,
-        MODEL_CLS=MODEL_CLS, **kwargs)
+    def __init__(self, hparams, MODEL_CLS=Seq2SeqNet, **kwargs):
+        super().__init__(hparams, MODEL_CLS=MODEL_CLS, **kwargs)
+
+    DEFAULT_ARGS = {
+        "agg": "mean",
+        "lstm_dropout": 0.22,
+        "hidden_size_power": 4.0,
+        "learning_rate": 0.001,
+        "lstm_layers": 4,
+        'bidirectional': False
+    }
 
-    DEFAULT_ARGS = {'agg': 'mean', 'lstm_dropout': 0.12013231612195126, 'hidden_out_size_power': 4.0, 'hidden_size_power': 7.0, 'learning_rate': 0.0022924639229335475, 'nhead_power': 2.0, 'nlayers_power': 4.0}
-    
     @staticmethod
     def add_suggest(trial):
-        # TODO make label name configurable
-        # TODO make data source configurable
         trial.suggest_loguniform("learning_rate", 1e-5, 1e-2)
         trial.suggest_uniform("lstm_dropout", 0, 0.75)
-        trial.suggest_categorical("hidden_size", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512])    
-        trial.suggest_categorical("lstm_layers", [1, 2, 4, 8])    
-        trial.suggest_categorical("bidirectional", [False, True])    
-        
+        trial.suggest_discrete_uniform("hidden_size_power", 3, 9, 1)
+        trial.suggest_int("lstm_layers", 1, 8)
+        trial.suggest_categorical("bidirectional", [False, True])
 
         trial._user_attrs = {
-                'batch_size': 16,
-                'grad_clip': 40,
-                'max_nb_epochs': 200,
-                'num_workers': 4,
-                'num_extra_target': 24*4,
-                'vis_i': '670',
-                'num_context': 24*4,
-                'input_size': 18,
-                'input_size_decoder': 17,
-                'context_in_target': True,
-                'output_size': 1
+            "batch_size": 16,
+            "grad_clip": 40,
+            "max_nb_epochs": 200,
+            "num_workers": 4,
+            "num_extra_target": 24 * 4,
+            "vis_i": "670",
+            "num_context": 24 * 4,
+            "input_size": 18,
+            "input_size_decoder": 17,
+            "context_in_target": False,
+            "output_size": 1,
         }
         return trial
diff --git a/neural_processes/models/lstm_std.py b/neural_processes/models/lstm_std.py
index 9fe764e..a9dec7b 100644
--- a/neural_processes/models/lstm_std.py
+++ b/neural_processes/models/lstm_std.py
@@ -24,15 +24,14 @@ from neural_processes.data.smart_meter import get_smartmeter_df
 from neural_processes.utils import ObjectDict
 from ..lightning import PL_Seq2Seq
 from torch.utils.data._utils.collate import default_collate
-
-def collate_fn(batch, sample=None):
-    return default_collate(batch)
+from ..logger import logger
+from ..utils import hparams_power
 
 
 class LSTMNet(nn.Module):
-
-    def __init__(self, hparams, _min_std = 0.05):
+    def __init__(self, hparams, _min_std=0.05):
         super().__init__()
+        hparams = hparams_power(hparams)
         self.hparams = hparams
         self._min_std = _min_std
 
@@ -44,9 +43,8 @@ class LSTMNet(nn.Module):
             bidirectional=self.hparams.bidirectional,
             dropout=self.hparams.lstm_dropout,
         )
-        self.hidden_out_size = (
-            self.hparams.hidden_size
-            * (self.hparams.bidirectional + 1)
+        self.hidden_out_size = self.hparams.hidden_size * (
+            self.hparams.bidirectional + 1
         )
         self.mean = nn.Linear(self.hidden_out_size, 1)
         self.std = nn.Linear(self.hidden_out_size, 1)
@@ -62,7 +60,12 @@ class LSTMNet(nn.Module):
 
         loss = None
         if target_y is not None:
-            loss = F.mse_loss(y_pred * loss_scale, y[:, -steps:, :] * loss_scale, reduction='none') / loss_scale
+            loss = (
+                F.mse_loss(
+                    y_pred * loss_scale, y[:, -steps:, :] * loss_scale, reduction="none"
+                )
+                / loss_scale
+            )
 
             assert torch.isfinite(loss)
 
@@ -70,35 +73,39 @@ class LSTMNet(nn.Module):
 
 
 class LSTM_PL_STD(PL_Seq2Seq):
-    def __init__(self, hparams,
-        MODEL_CLS=LSTMNet, **kwargs):
-        super().__init__(hparams,
-        MODEL_CLS=MODEL_CLS, **kwargs)
+    def __init__(self, hparams, MODEL_CLS=LSTMNet, **kwargs):
+        super().__init__(hparams, MODEL_CLS=MODEL_CLS, **kwargs)
 
-    DEFAULT_ARGS = {'bidirectional': False, 'hidden_size_power': 4, 'learning_rate': 0.0010825329363784934, 'lstm_dropout': 0.3905792111699782, 'lstm_layers': 4}
+    DEFAULT_ARGS = {
+        "bidirectional": False,
+        "hidden_size_power": 4,
+        "learning_rate": 0.001,
+        "lstm_dropout": 0.39,
+        "lstm_layers": 4,
+        "bidirectional": False,
+    }
 
     @staticmethod
     def add_suggest(trial):
         trial.suggest_loguniform("learning_rate", 1e-5, 1e-2)
-        trial.suggest_uniform("lstm_dropout", 0, 0.75)
-        trial.suggest_categorical("hidden_size", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512])    
-        trial.suggest_categorical("lstm_layers", [1, 2, 4, 8])
+        trial.suggest_uniform("lstm_dropout", 0, 0.85)
+        trial.suggest_discrete_uniform("hidden_size_power", 3, 9, 1)
+        trial.suggest_int("lstm_layers", 1, 8)
         trial.suggest_categorical("bidirectional", [False, True])
-        
+
         # constants
         trial._user_attrs = {
-            'batch_size': 16,
-            'grad_clip': 40,
-            'max_nb_epochs': 200,
-            'num_workers': 4,
-            'num_extra_target': 24*4,
-            'vis_i': '670',
-            'num_context': 24*4,
-            'input_size': 18,
-            'input_size_decoder': 17,
-            'context_in_target': True,
-            'output_size': 1,
-            'patience': 3,
+            "batch_size": 16,
+            "grad_clip": 40,
+            "max_nb_epochs": 200,
+            "num_workers": 4,
+            "num_extra_target": 24 * 4,
+            "vis_i": "670",
+            "num_context": 24 * 4,
+            "input_size": 18,
+            "input_size_decoder": 17,
+            "context_in_target": False,
+            "output_size": 1,
+            "patience": 3,
         }
         return trial
-
diff --git a/neural_processes/models/neural_process/lightning.py b/neural_processes/models/neural_process/lightning.py
index 569802f..1f485fd 100644
--- a/neural_processes/models/neural_process/lightning.py
+++ b/neural_processes/models/neural_process/lightning.py
@@ -21,14 +21,14 @@ class PL_NeuralProcess(PL_Seq2Seq):
         'det_enc_cross_attn_type': 'multihead',
         'det_enc_self_attn_type': 'uniform',
         'dropout': 0,
-        'hidden_dim': 128,
-        'latent_dim': 128,
+        'hidden_dim_power': 7,
+        'latent_dim_power': 7,
         'latent_enc_self_attn_type': 'uniform',
         'learning_rate': 0.002,
         'n_decoder_layers': 4,
         'n_det_encoder_layers': 4,
-        'n_latent_encoder_layers': 2,
-        'num_heads': 8,
+        'n_latent_encoder_layers_power': 1,
+        'num_heads_power': 3,
         'use_deterministic_path': True,
         'use_lvar': True,
         'use_self_attn': True,
@@ -37,16 +37,23 @@ class PL_NeuralProcess(PL_Seq2Seq):
 
     @staticmethod
     def add_suggest(trial):        
-        trial.suggest_loguniform("learning_rate", 1e-5, 1e-2)
-
-        trial.suggest_categorical("hidden_dim", [8*2**i for i in range(8)])
-        trial.suggest_categorical("latent_dim", [8*2**i for i in range(8)])
-        
+        trial.suggest_loguniform("learning_rate", 1e-6, 1e-2)
         trial.suggest_int("attention_layers", 1, 4)
-        trial.suggest_categorical("n_latent_encoder_layers", [1, 2, 4, 6, 8, 12])
-        trial.suggest_categorical("n_det_encoder_layers", [1, 2, 4, 6, 8, 12])
-        trial.suggest_categorical("n_decoder_layers", [1, 2, 4, 6, 8, 12])
-        trial.suggest_int("num_heads", 8, 8)
+        trial.suggest_discrete_uniform("num_heads_power", 2, 4, 1)
+
+        trial.suggest_discrete_uniform(
+            "hidden_dim_power", 3, 11, 1
+        )
+        trial.suggest_discrete_uniform(
+            "latent_dim_power", 3, 11, 1
+        )
+        trial.suggest_int(
+            "n_latent_encoder_layers", 1, 11
+        )
+
+        trial.suggest_int("n_latent_encoder_layers", 1, 12)
+        trial.suggest_int("n_det_encoder_layers", 1, 12)
+        trial.suggest_int("n_decoder_layers", 1, 12)
 
         trial.suggest_uniform("dropout", 0, 0.9)
         trial.suggest_uniform("attention_dropout", 0, 0.9)
@@ -72,7 +79,7 @@ class PL_NeuralProcess(PL_Seq2Seq):
             'vis_i': '670',
             'num_extra_target': 24*4,
             'x_dim': 18,
-            'context_in_target': True,
+            'context_in_target': False,
             'y_dim': 1,
             'patience': 3,
             'min_std': 0.005,
diff --git a/neural_processes/models/neural_process/model.py b/neural_processes/models/neural_process/model.py
index 54be582..ec3fa2c 100644
--- a/neural_processes/models/neural_process/model.py
+++ b/neural_processes/models/neural_process/model.py
@@ -6,7 +6,7 @@ import math
 
 from neural_processes.modules import BatchNormSequence, BatchMLP, Attention, LSTMBlock
 from neural_processes.utils import kl_loss_var, log_prob_sigma
-
+from neural_processes.utils import hparams_power
 
 class LatentEncoder(nn.Module):
     def __init__(
@@ -195,6 +195,7 @@ class NeuralProcess(nn.Module):
 
     @staticmethod
     def FROM_HPARAMS(hparams):
+        hparams = hparams_power(hparams)
         return NeuralProcess(**hparams)
     
     def __init__(self,
diff --git a/neural_processes/models/transformer.py b/neural_processes/models/transformer.py
index 92917ac..bed3b1d 100644
--- a/neural_processes/models/transformer.py
+++ b/neural_processes/models/transformer.py
@@ -9,15 +9,15 @@ from torch.nn import functional as F
 from torch.utils.data import DataLoader
 
 from neural_processes.lightning import PL_Seq2Seq
+from ..logger import logger
 
 class NetTransformer(nn.Module):
     def __init__(self, hparams):
         super().__init__()
-        hparams["nlayers"] = int(2 ** hparams["nlayers_power"])
-        hparams["hidden_size"] = int(2**hparams["hidden_size_power"])
-        hparams["hidden_out_size"] = int(2 ** hparams["hidden_out_size_power"])
-        hparams["nhead"] = int(2 ** hparams["nhead_power"])
-        logger.debug(f"{type(self)} hparams {hparams}")
+        for k in hparams.keys():
+            if k.endswith("_power"):
+                k_new = k.replace("_power", "")
+                hparams[k_new] = int(2 ** hparams[k])
         self.hparams = hparams
 
         hidden_out_size = self.hparams.hidden_out_size
@@ -148,7 +148,7 @@ class PL_Transformer(PL_Seq2Seq):
         )
         trial.suggest_discrete_uniform("hidden_out_size_power", 2, 9, 1)
         trial.suggest_discrete_uniform("nhead_power", 1, 4, 1)
-        trial.suggest_discrete_uniform("nlayers_power", 1, 5, 1)      
+        trial.suggest_int("nlayers_power", 1, 12)      
 
         user_attrs_default = {
             "batch_size": 16,
@@ -159,6 +159,9 @@ class PL_Transformer(PL_Seq2Seq):
             "input_size": 6,
             "output_size": 1,
             "label_steps": 24,
+            "nan_value": -99.9,
+            'context_in_target': False,
+            'patience': 3,
         }
         [trial.set_user_attr(k, v) for k, v in user_attrs_default.items()]
         [trial.set_user_attr(k, v) for k, v in user_attrs.items()]
diff --git a/neural_processes/models/transformer_seq2seq.py b/neural_processes/models/transformer_seq2seq.py
index 6a15179..411b251 100644
--- a/neural_processes/models/transformer_seq2seq.py
+++ b/neural_processes/models/transformer_seq2seq.py
@@ -27,10 +27,13 @@ from neural_processes.modules import BatchNormSequence
 
 from neural_processes.utils import ObjectDict
 from neural_processes.lightning import PL_Seq2Seq
+from ..logger import logger
+from ..utils import hparams_power
 
 class TransformerSeq2SeqNet(nn.Module):
     def __init__(self, hparams, _min_std = 0.05):
         super().__init__()
+        hparams = hparams_power(hparams)
         self.hparams = hparams
         self._min_std = _min_std
 
@@ -134,7 +137,7 @@ class TransformerSeq2Seq_PL(PL_Seq2Seq):
         super().__init__(hparams,
         MODEL_CLS=MODEL_CLS, **kwargs)
     
-    DEFAULT_ARGS = {'agg': 'mean', 'attention_dropout': 0.12013231612195126, 'hidden_out_size_power': 4.0, 'hidden_size_power': 7.0, 'learning_rate': 0.0022924639229335475, 'nhead_power': 2.0, 'nlayers_power': 4.0}
+    DEFAULT_ARGS = {'agg': 'mean', 'attention_dropout': 0.12, 'hidden_out_size_power': 4, 'hidden_size_power': 7, 'learning_rate': 0.0023, 'nhead_power': 2, 'nlayers_power': 4}
 
     @staticmethod
     def add_suggest(trial: optuna.Trial):
@@ -155,10 +158,12 @@ class TransformerSeq2Seq_PL(PL_Seq2Seq):
         """
         trial.suggest_loguniform("learning_rate", 1e-6, 1e-2)
         trial.suggest_uniform("attention_dropout", 0, 0.75)
-        trial.suggest_categorical("hidden_size", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048])    
-        trial.suggest_categorical("hidden_out_size", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048])    
-        trial.suggest_categorical("nlayers", [1, 2, 4, 6, 8, 16, 32])
-        trial.suggest_categorical("nhead", [1, 2, 8, 16])
+        trial.suggest_discrete_uniform(
+            "hidden_size_power", 2, 10, 1
+        ) 
+        trial.suggest_discrete_uniform("hidden_out_size_power", 2, 9, 1) 
+        trial.suggest_discrete_uniform("nhead_power", 1, 4, 1)
+        trial.suggest_int("nlayers", 1, 12)
 
         trial._user_attrs = {
             'batch_size': 16,
@@ -170,7 +175,7 @@ class TransformerSeq2Seq_PL(PL_Seq2Seq):
             'num_context': 24*4,
             'input_size': 18,
             'input_size_decoder': 17,
-            'context_in_target': True,
+            'context_in_target': False,
             'output_size': 1,
             'patience': 3,
         }
diff --git a/neural_processes/train.py b/neural_processes/train.py
index b7d4345..47afde5 100644
--- a/neural_processes/train.py
+++ b/neural_processes/train.py
@@ -106,7 +106,7 @@ def run_trial(
 
     # Add user attributes
     trial._user_attrs.update(user_attrs)
-    print('trial', trial)
+    print('trial', trial, trial.params, trial.user_attrs)
 
     model, trainer = main(
         trial, PL_MODEL_CLS, name=name, MODEL_DIR=MODEL_DIR, train=False, prune=False
diff --git a/neural_processes/utils.py b/neural_processes/utils.py
index a83348d..7958a87 100644
--- a/neural_processes/utils.py
+++ b/neural_processes/utils.py
@@ -55,28 +55,63 @@ class PyTorchLightningPruningCallback(EarlyStopping):
             raise optuna.exceptions.TrialPruned(message)
 
 
+# class ObjectDict(dict):
+#     """
+#     Interface similar to an argparser
+#     """
+
+#     def __init__(self):
+#         pass
+
+#     def __setattr__(self, attr, value):
+#         self[attr] = value
+#         return self[attr]
+
+#     def __getattr__(self, attr):
+#         if attr.startswith("_"):
+#             # https://stackoverflow.com/questions/10364332/how-to-pickle-python-object-derived-from-dict
+#             raise AttributeError
+#         try:
+#             return super().__getitem__(attr)
+#         except KeyError:
+#             # cPickle expects __getattr__ to raise AttributeError, not KeyError.
+#             raise AttributeError(self._KeyErrorString(name))
+
+#     @property
+#     def __dict__(self):
+#         return dict(self)
+
 class ObjectDict(dict):
     """
-    Interface similar to an argparser
+    easy way to represent (hyper)parameters.
+
+    https://stackoverflow.com/a/50613966/221742
     """
+    __getattr__ = dict.__getitem__
+    __setattr__ = dict.__setitem__
+    __delattr__ = dict.__delitem__
 
-    def __init__(self):
-        pass
+    def __getstate__(self):
+        return self
 
-    def __setattr__(self, attr, value):
-        self[attr] = value
-        return self[attr]
+    def __setstate__(self, state):
+        self.update(state)
 
-    def __getattr__(self, attr):
-        if attr.startswith("_"):
-            # https://stackoverflow.com/questions/10364332/how-to-pickle-python-object-derived-from-dict
-            raise AttributeError
-        return dict(self)[attr]
+    def copy(self, **extra_params):
+        return ObjectDict(**self, **extra_params)
 
-    @property
-    def __dict__(self):
-        return dict(self)
 
+def hparams_power(hparams):
+    """Some value we want to go up in powers of 2
+    
+    So any hyper param that ends in power will be used this way.
+    """
+    hparams_old = hparams.copy()
+    for k in hparams_old.keys():
+        if k.endswith("_power"):
+            k_new = k.replace("_power", "")
+            hparams[k_new] = int(2 ** hparams[k])
+    return hparams
 
 def log_prob_sigma(value, loc, log_scale):
     """A slightly more stable (not confirmed yet) log prob taking in log_var instead of scale.
diff --git a/requirements.txt b/requirements.txt
index 7cc11f8..1d4be14 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,7 @@
+# local package
+-e .
+
+# external requirements
 torch>=1.3.0
 tqdm
 pandas
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..a9c8669
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,10 @@
+from setuptools import find_packages, setup
+
+setup(
+    name='neural_processes',
+    packages=find_packages(),
+    version='0.1.0',
+    description='Attentive Neural Processes',
+    author='wassname',
+    license='Apachev2',
+)
diff --git a/smartmeters-ANP-RNN.ipynb b/smartmeters-ANP-RNN.ipynb
index d60197e..5f52e05 100644
--- a/smartmeters-ANP-RNN.ipynb
+++ b/smartmeters-ANP-RNN.ipynb
@@ -42,8 +42,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-11T05:31:36.604028Z",
-     "start_time": "2020-04-11T05:31:34.179270Z"
+     "end_time": "2020-04-11T07:33:41.440376Z",
+     "start_time": "2020-04-11T07:33:39.040281Z"
     }
    },
    "outputs": [],
@@ -72,8 +72,8 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-11T05:31:36.672837Z",
-     "start_time": "2020-04-11T05:31:36.609199Z"
+     "end_time": "2020-04-11T07:33:41.490347Z",
+     "start_time": "2020-04-11T07:33:41.442977Z"
     }
    },
    "outputs": [],
@@ -88,8 +88,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-11T05:31:36.811071Z",
-     "start_time": "2020-04-11T05:31:36.675979Z"
+     "end_time": "2020-04-11T07:33:41.618208Z",
+     "start_time": "2020-04-11T07:33:41.493249Z"
     }
    },
    "outputs": [
@@ -127,8 +127,8 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-11T05:31:36.865423Z",
-     "start_time": "2020-04-11T05:31:36.814579Z"
+     "end_time": "2020-04-11T07:33:41.670012Z",
+     "start_time": "2020-04-11T07:33:41.621030Z"
     }
    },
    "outputs": [],
@@ -143,8 +143,8 @@
    "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-11T05:31:36.918980Z",
-     "start_time": "2020-04-11T05:31:36.868490Z"
+     "end_time": "2020-04-11T07:33:41.721306Z",
+     "start_time": "2020-04-11T07:33:41.672405Z"
     }
    },
    "outputs": [],
@@ -166,8 +166,8 @@
    "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-11T05:31:45.902849Z",
-     "start_time": "2020-04-11T05:31:36.921263Z"
+     "end_time": "2020-04-11T07:33:50.843115Z",
+     "start_time": "2020-04-11T07:33:41.723616Z"
     }
    },
    "outputs": [],
@@ -180,15 +180,15 @@
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-11T05:31:46.602152Z",
-     "start_time": "2020-04-11T05:31:45.906531Z"
+     "end_time": "2020-04-11T07:34:06.000515Z",
+     "start_time": "2020-04-11T07:33:50.846196Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f27ce25c4a8>"
+       "<matplotlib.legend.Legend at 0x7f2b15a3ffd0>"
       ]
      },
      "execution_count": 7,
@@ -229,8 +229,8 @@
    "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-11T05:31:46.658638Z",
-     "start_time": "2020-04-11T05:31:46.606018Z"
+     "end_time": "2020-04-11T07:34:06.056421Z",
+     "start_time": "2020-04-11T07:34:06.004945Z"
     }
    },
    "outputs": [],
@@ -256,8 +256,8 @@
    "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-04-11T05:31:46.715140Z",
-     "start_time": "2020-04-11T05:31:46.661863Z"
+     "end_time": "2020-04-11T07:34:06.108308Z",
+     "start_time": "2020-04-11T07:34:06.059793Z"
     }
    },
    "outputs": [],
@@ -278,10 +278,46 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-11T07:34:41.904630Z",
+     "start_time": "2020-04-11T07:34:41.800002Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "now run `tensorboard --logdir lightning_logs`\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "The value 1 of the parameter 'n_latent_encoder_layers_power' is out of the range of the distribution DiscreteUniformDistribution(high=11, low=3, q=1).",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-11-280de67a1f89>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m     },\n\u001b[1;32m      5\u001b[0m     \u001b[0muser_attrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefault_user_attrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mPL_MODEL_CLS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPL_NeuralProcess\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m )\n",
+      "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/attentive-neural-processes/neural_processes/train.py\u001b[0m in \u001b[0;36mrun_trial\u001b[0;34m(name, PL_MODEL_CLS, params, user_attrs, MODEL_DIR, plot_from_loader)\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0;31m# Make trial\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[0mtrial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptuna\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFixedTrial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m     \u001b[0mtrial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPL_MODEL_CLS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_suggest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0;31m# Add auto number\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/attentive-neural-processes/neural_processes/models/neural_process/lightning.py\u001b[0m in \u001b[0;36madd_suggest\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m     49\u001b[0m         )\n\u001b[1;32m     50\u001b[0m         trial.suggest_discrete_uniform(\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0;34m\"n_latent_encoder_layers_power\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         )\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/trial.py\u001b[0m in \u001b[0;36msuggest_discrete_uniform\u001b[0;34m(self, name, low, high, q)\u001b[0m\n\u001b[1;32m    681\u001b[0m         \u001b[0mhigh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_adjust_discrete_uniform_high\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m         \u001b[0mdiscrete\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdistributions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDiscreteUniformDistribution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhigh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 683\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_suggest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiscrete\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    684\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    685\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msuggest_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/trial.py\u001b[0m in \u001b[0;36m_suggest\u001b[0;34m(self, name, distribution)\u001b[0m\n\u001b[1;32m    705\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdistribution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_contains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam_value_in_internal_repr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    706\u001b[0m             raise ValueError(\"The value {} of the parameter '{}' is out of \"\n\u001b[0;32m--> 707\u001b[0;31m                              \"the range of the distribution {}.\".format(value, name, distribution))\n\u001b[0m\u001b[1;32m    708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    709\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_distributions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: The value 1 of the parameter 'n_latent_encoder_layers_power' is out of the range of the distribution DiscreteUniformDistribution(high=11, low=3, q=1)."
+     ]
+    }
+   ],
    "source": [
-    "## ANP-RNN"
+    "trial, trainer, model = run_trial(\n",
+    "    name=\"anp-rnn\",\n",
+    "    params={\n",
+    "    },\n",
+    "    user_attrs = default_user_attrs,\n",
+    "    PL_MODEL_CLS=PL_NeuralProcess\n",
+    ")"
    ]
   },
   {
@@ -289,7 +325,60 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-04-11T05:31:34.200Z"
+     "end_time": "2020-04-11T07:34:41.908866Z",
+     "start_time": "2020-04-11T07:33:39.100Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "trial, trainer, model = run_trial(name=\"PL_Transformer\",\n",
+    "                                  params={\n",
+    "                                  },\n",
+    "                                  user_attrs={**default_user_attrs, 'context_in_target': False},\n",
+    "                                  PL_MODEL_CLS=PL_Transformer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-11T07:34:41.910251Z",
+     "start_time": "2020-04-11T07:33:39.200Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "trial, trainer, model = run_trial(name=\"TransformerSeq2Seq_PL\",\n",
+    "                                  params={},\n",
+    "                                  user_attrs=default_user_attrs,\n",
+    "                                  PL_MODEL_CLS=TransformerSeq2Seq_PL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-11T07:34:41.911846Z",
+     "start_time": "2020-04-11T07:33:39.200Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "trial, trainer, model = run_trial(name=\"LSTM_PL_STD\",\n",
+    "                                  params={},\n",
+    "                                  user_attrs=default_user_attrs,\n",
+    "                                  PL_MODEL_CLS=LSTM_PL_STD)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-11T07:34:41.796127Z",
+     "start_time": "2020-04-11T07:34:06.110618Z"
     }
    },
    "outputs": [
@@ -298,136 +387,24 @@
      "output_type": "stream",
      "text": [
       "now run `tensorboard --logdir lightning_logs`\n",
-      "trial.number -31\n",
-      "trial <optuna.trial.FixedTrial object at 0x7f27cf19b860>\n",
+      "trial.number -4\n",
+      "trial <optuna.trial.FixedTrial object at 0x7f2b11fb2278> {'learning_rate': 0.001, 'lstm_dropout': 0.22, 'hidden_size_power': 4.0, 'lstm_layers': 4, 'bidirectional': False} {'batch_size': 16, 'grad_clip': 40, 'max_nb_epochs': 200, 'num_workers': 3, 'num_extra_target': 96, 'vis_i': '670', 'num_context': 96, 'input_size': 18, 'input_size_decoder': 17, 'context_in_target': True, 'output_size': 1, 'x_dim': 17, 'y_dim': 1, 'min_std': 0.005, 'patience': 2}\n",
       "INFO:root:GPU available: True, used: True\n",
       "INFO:root:VISIBLE GPUS: 0\n",
       "INFO:root:\n",
-      "    | Name                                                                       | Type                 | Params\n",
-      "----------------------------------------------------------------------------------------------------------------\n",
-      "0   | _model                                                                     | NeuralProcess        | 1 M   \n",
-      "1   | _model.norm_x                                                              | BatchNormSequence    | 34    \n",
-      "2   | _model.norm_x.norm                                                         | BatchNorm1d          | 34    \n",
-      "3   | _model.norm_y                                                              | BatchNormSequence    | 2     \n",
-      "4   | _model.norm_y.norm                                                         | BatchNorm1d          | 2     \n",
-      "5   | _model._lstm_x                                                             | LSTM                 | 207 K \n",
-      "6   | _model._lstm_y                                                             | LSTM                 | 199 K \n",
-      "7   | _model._latent_encoder                                                     | LatentEncoder        | 98 K  \n",
-      "8   | _model._latent_encoder._encoder                                            | BatchMLP             | 49 K  \n",
-      "9   | _model._latent_encoder._encoder.initial                                    | NPBlockRelu2d        | 32 K  \n",
-      "10  | _model._latent_encoder._encoder.initial.linear                             | Linear               | 32 K  \n",
-      "11  | _model._latent_encoder._encoder.initial.act                                | ReLU                 | 0     \n",
-      "12  | _model._latent_encoder._encoder.initial.dropout                            | Dropout2d            | 0     \n",
-      "13  | _model._latent_encoder._encoder.encoder                                    | Sequential           | 0     \n",
-      "14  | _model._latent_encoder._encoder.final                                      | Linear               | 16 K  \n",
-      "15  | _model._latent_encoder._self_attention                                     | Attention            | 0     \n",
-      "16  | _model._latent_encoder._penultimate_layer                                  | Linear               | 16 K  \n",
-      "17  | _model._latent_encoder._mean                                               | Linear               | 16 K  \n",
-      "18  | _model._latent_encoder._log_var                                            | Linear               | 16 K  \n",
-      "19  | _model._deterministic_encoder                                              | DeterministicEncoder | 672 K \n",
-      "20  | _model._deterministic_encoder._d_encoder                                   | BatchMLP             | 82 K  \n",
-      "21  | _model._deterministic_encoder._d_encoder.initial                           | NPBlockRelu2d        | 32 K  \n",
-      "22  | _model._deterministic_encoder._d_encoder.initial.linear                    | Linear               | 32 K  \n",
-      "23  | _model._deterministic_encoder._d_encoder.initial.act                       | ReLU                 | 0     \n",
-      "24  | _model._deterministic_encoder._d_encoder.initial.dropout                   | Dropout2d            | 0     \n",
-      "25  | _model._deterministic_encoder._d_encoder.encoder                           | Sequential           | 32 K  \n",
-      "26  | _model._deterministic_encoder._d_encoder.encoder.0                         | NPBlockRelu2d        | 16 K  \n",
-      "27  | _model._deterministic_encoder._d_encoder.encoder.0.linear                  | Linear               | 16 K  \n",
-      "28  | _model._deterministic_encoder._d_encoder.encoder.0.act                     | ReLU                 | 0     \n",
-      "29  | _model._deterministic_encoder._d_encoder.encoder.0.dropout                 | Dropout2d            | 0     \n",
-      "30  | _model._deterministic_encoder._d_encoder.encoder.1                         | NPBlockRelu2d        | 16 K  \n",
-      "31  | _model._deterministic_encoder._d_encoder.encoder.1.linear                  | Linear               | 16 K  \n",
-      "32  | _model._deterministic_encoder._d_encoder.encoder.1.act                     | ReLU                 | 0     \n",
-      "33  | _model._deterministic_encoder._d_encoder.encoder.1.dropout                 | Dropout2d            | 0     \n",
-      "34  | _model._deterministic_encoder._d_encoder.final                             | Linear               | 16 K  \n",
-      "35  | _model._deterministic_encoder._self_attention                              | Attention            | 0     \n",
-      "36  | _model._deterministic_encoder._cross_attention                             | Attention            | 590 K \n",
-      "37  | _model._deterministic_encoder._cross_attention.batch_mlp_k                 | BatchMLP             | 32 K  \n",
-      "38  | _model._deterministic_encoder._cross_attention.batch_mlp_k.initial         | NPBlockRelu2d        | 16 K  \n",
-      "39  | _model._deterministic_encoder._cross_attention.batch_mlp_k.initial.linear  | Linear               | 16 K  \n",
-      "40  | _model._deterministic_encoder._cross_attention.batch_mlp_k.initial.act     | ReLU                 | 0     \n",
-      "41  | _model._deterministic_encoder._cross_attention.batch_mlp_k.initial.dropout | Dropout2d            | 0     \n",
-      "42  | _model._deterministic_encoder._cross_attention.batch_mlp_k.encoder         | Sequential           | 0     \n",
-      "43  | _model._deterministic_encoder._cross_attention.batch_mlp_k.final           | Linear               | 16 K  \n",
-      "44  | _model._deterministic_encoder._cross_attention.batch_mlp_q                 | BatchMLP             | 32 K  \n",
-      "45  | _model._deterministic_encoder._cross_attention.batch_mlp_q.initial         | NPBlockRelu2d        | 16 K  \n",
-      "46  | _model._deterministic_encoder._cross_attention.batch_mlp_q.initial.linear  | Linear               | 16 K  \n",
-      "47  | _model._deterministic_encoder._cross_attention.batch_mlp_q.initial.act     | ReLU                 | 0     \n",
-      "48  | _model._deterministic_encoder._cross_attention.batch_mlp_q.initial.dropout | Dropout2d            | 0     \n",
-      "49  | _model._deterministic_encoder._cross_attention.batch_mlp_q.encoder         | Sequential           | 0     \n",
-      "50  | _model._deterministic_encoder._cross_attention.batch_mlp_q.final           | Linear               | 16 K  \n",
-      "51  | _model._deterministic_encoder._cross_attention._W_k                        | ModuleList           | 131 K \n",
-      "52  | _model._deterministic_encoder._cross_attention._W_k.0                      | AttnLinear           | 16 K  \n",
-      "53  | _model._deterministic_encoder._cross_attention._W_k.0.linear               | Linear               | 16 K  \n",
-      "54  | _model._deterministic_encoder._cross_attention._W_k.1                      | AttnLinear           | 16 K  \n",
-      "55  | _model._deterministic_encoder._cross_attention._W_k.1.linear               | Linear               | 16 K  \n",
-      "56  | _model._deterministic_encoder._cross_attention._W_k.2                      | AttnLinear           | 16 K  \n",
-      "57  | _model._deterministic_encoder._cross_attention._W_k.2.linear               | Linear               | 16 K  \n",
-      "58  | _model._deterministic_encoder._cross_attention._W_k.3                      | AttnLinear           | 16 K  \n",
-      "59  | _model._deterministic_encoder._cross_attention._W_k.3.linear               | Linear               | 16 K  \n",
-      "60  | _model._deterministic_encoder._cross_attention._W_k.4                      | AttnLinear           | 16 K  \n",
-      "61  | _model._deterministic_encoder._cross_attention._W_k.4.linear               | Linear               | 16 K  \n",
-      "62  | _model._deterministic_encoder._cross_attention._W_k.5                      | AttnLinear           | 16 K  \n",
-      "63  | _model._deterministic_encoder._cross_attention._W_k.5.linear               | Linear               | 16 K  \n",
-      "64  | _model._deterministic_encoder._cross_attention._W_k.6                      | AttnLinear           | 16 K  \n",
-      "65  | _model._deterministic_encoder._cross_attention._W_k.6.linear               | Linear               | 16 K  \n",
-      "66  | _model._deterministic_encoder._cross_attention._W_k.7                      | AttnLinear           | 16 K  \n",
-      "67  | _model._deterministic_encoder._cross_attention._W_k.7.linear               | Linear               | 16 K  \n",
-      "68  | _model._deterministic_encoder._cross_attention._W_v                        | ModuleList           | 131 K \n",
-      "69  | _model._deterministic_encoder._cross_attention._W_v.0                      | AttnLinear           | 16 K  \n",
-      "70  | _model._deterministic_encoder._cross_attention._W_v.0.linear               | Linear               | 16 K  \n",
-      "71  | _model._deterministic_encoder._cross_attention._W_v.1                      | AttnLinear           | 16 K  \n",
-      "72  | _model._deterministic_encoder._cross_attention._W_v.1.linear               | Linear               | 16 K  \n",
-      "73  | _model._deterministic_encoder._cross_attention._W_v.2                      | AttnLinear           | 16 K  \n",
-      "74  | _model._deterministic_encoder._cross_attention._W_v.2.linear               | Linear               | 16 K  \n",
-      "75  | _model._deterministic_encoder._cross_attention._W_v.3                      | AttnLinear           | 16 K  \n",
-      "76  | _model._deterministic_encoder._cross_attention._W_v.3.linear               | Linear               | 16 K  \n",
-      "77  | _model._deterministic_encoder._cross_attention._W_v.4                      | AttnLinear           | 16 K  \n",
-      "78  | _model._deterministic_encoder._cross_attention._W_v.4.linear               | Linear               | 16 K  \n",
-      "79  | _model._deterministic_encoder._cross_attention._W_v.5                      | AttnLinear           | 16 K  \n",
-      "80  | _model._deterministic_encoder._cross_attention._W_v.5.linear               | Linear               | 16 K  \n",
-      "81  | _model._deterministic_encoder._cross_attention._W_v.6                      | AttnLinear           | 16 K  \n",
-      "82  | _model._deterministic_encoder._cross_attention._W_v.6.linear               | Linear               | 16 K  \n",
-      "83  | _model._deterministic_encoder._cross_attention._W_v.7                      | AttnLinear           | 16 K  \n",
-      "84  | _model._deterministic_encoder._cross_attention._W_v.7.linear               | Linear               | 16 K  \n",
-      "85  | _model._deterministic_encoder._cross_attention._W_q                        | ModuleList           | 131 K \n",
-      "86  | _model._deterministic_encoder._cross_attention._W_q.0                      | AttnLinear           | 16 K  \n",
-      "87  | _model._deterministic_encoder._cross_attention._W_q.0.linear               | Linear               | 16 K  \n",
-      "88  | _model._deterministic_encoder._cross_attention._W_q.1                      | AttnLinear           | 16 K  \n",
-      "89  | _model._deterministic_encoder._cross_attention._W_q.1.linear               | Linear               | 16 K  \n",
-      "90  | _model._deterministic_encoder._cross_attention._W_q.2                      | AttnLinear           | 16 K  \n",
-      "91  | _model._deterministic_encoder._cross_attention._W_q.2.linear               | Linear               | 16 K  \n",
-      "92  | _model._deterministic_encoder._cross_attention._W_q.3                      | AttnLinear           | 16 K  \n",
-      "93  | _model._deterministic_encoder._cross_attention._W_q.3.linear               | Linear               | 16 K  \n",
-      "94  | _model._deterministic_encoder._cross_attention._W_q.4                      | AttnLinear           | 16 K  \n",
-      "95  | _model._deterministic_encoder._cross_attention._W_q.4.linear               | Linear               | 16 K  \n",
-      "96  | _model._deterministic_encoder._cross_attention._W_q.5                      | AttnLinear           | 16 K  \n",
-      "97  | _model._deterministic_encoder._cross_attention._W_q.5.linear               | Linear               | 16 K  \n",
-      "98  | _model._deterministic_encoder._cross_attention._W_q.6                      | AttnLinear           | 16 K  \n",
-      "99  | _model._deterministic_encoder._cross_attention._W_q.6.linear               | Linear               | 16 K  \n",
-      "100 | _model._deterministic_encoder._cross_attention._W_q.7                      | AttnLinear           | 16 K  \n",
-      "101 | _model._deterministic_encoder._cross_attention._W_q.7.linear               | Linear               | 16 K  \n",
-      "102 | _model._deterministic_encoder._cross_attention._W                          | AttnLinear           | 131 K \n",
-      "103 | _model._deterministic_encoder._cross_attention._W.linear                   | Linear               | 131 K \n",
-      "104 | _model._decoder                                                            | Decoder              | 607 K \n",
-      "105 | _model._decoder._target_transform                                          | Linear               | 16 K  \n",
-      "106 | _model._decoder._decoder                                                   | BatchMLP             | 590 K \n",
-      "107 | _model._decoder._decoder.initial                                           | NPBlockRelu2d        | 147 K \n",
-      "108 | _model._decoder._decoder.initial.linear                                    | Linear               | 147 K \n",
-      "109 | _model._decoder._decoder.initial.act                                       | ReLU                 | 0     \n",
-      "110 | _model._decoder._decoder.initial.dropout                                   | Dropout2d            | 0     \n",
-      "111 | _model._decoder._decoder.encoder                                           | Sequential           | 294 K \n",
-      "112 | _model._decoder._decoder.encoder.0                                         | NPBlockRelu2d        | 147 K \n",
-      "113 | _model._decoder._decoder.encoder.0.linear                                  | Linear               | 147 K \n",
-      "114 | _model._decoder._decoder.encoder.0.act                                     | ReLU                 | 0     \n",
-      "115 | _model._decoder._decoder.encoder.0.dropout                                 | Dropout2d            | 0     \n",
-      "116 | _model._decoder._decoder.encoder.1                                         | NPBlockRelu2d        | 147 K \n",
-      "117 | _model._decoder._decoder.encoder.1.linear                                  | Linear               | 147 K \n",
-      "118 | _model._decoder._decoder.encoder.1.act                                     | ReLU                 | 0     \n",
-      "119 | _model._decoder._decoder.encoder.1.dropout                                 | Dropout2d            | 0     \n",
-      "120 | _model._decoder._decoder.final                                             | Linear               | 147 K \n",
-      "121 | _model._decoder._mean                                                      | Linear               | 385   \n",
-      "122 | _model._decoder._std                                                       | Linear               | 385   \n"
+      "   | Name                           | Type               | Params\n",
+      "------------------------------------------------------------------\n",
+      "0  | _model                         | Seq2SeqNet         | 18 K  \n",
+      "1  | _model.norm_input              | BatchNormSequence  | 36    \n",
+      "2  | _model.norm_input.norm         | BatchNorm1d        | 36    \n",
+      "3  | _model.encoder                 | LSTM               | 8 K   \n",
+      "4  | _model.multihead_attn          | MultiheadAttention | 1 K   \n",
+      "5  | _model.multihead_attn.out_proj | Linear             | 272   \n",
+      "6  | _model.norm_target             | BatchNormSequence  | 34    \n",
+      "7  | _model.norm_target.norm        | BatchNorm1d        | 34    \n",
+      "8  | _model.decoder                 | LSTM               | 8 K   \n",
+      "9  | _model.mean                    | Linear             | 17    \n",
+      "10 | _model.std                     | Linear             | 17    \n"
      ]
     },
     {
@@ -446,7 +423,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -460,14 +437,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "step 0, {'val_loss': '0.44150611758232117', 'val/loss_mse': '0.2931194007396698', 'val/loss_p': '0.44150611758232117', 'val/sigma': '1.0362932682037354'}\n",
+      "step 0, {'val_loss': '0.005343312863260508', 'val/loss_mse': '0.0009845279855653644', 'val/loss_p': '0.005343312863260508', 'val/sigma': '0.767909824848175'}\n",
       "\r"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c1091128cecf44d5a9a0973151c2f7e7",
+       "model_id": "a5b3f808530c47fb96d58b9302472726",
        "version_major": 2,
        "version_minor": 0
       },
@@ -477,8 +454,97 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Detected KeyboardInterrupt, attempting graceful shutdown...\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8bbc198529744059b51c19cbca291808",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, description='Testing', layout=Layout(flex='2'), max=356.0, style=Progr…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "KeyboardInterrupt, skipping rest of testing\n"
+     ]
     }
    ],
+   "source": [
+    "trial, trainer, model = run_trial(name=\"LSTMSeq2Seq_PL\",\n",
+    "                                  params={},\n",
+    "                                  user_attrs=default_user_attrs,\n",
+    "                                  PL_MODEL_CLS=LSTMSeq2Seq_PL)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ANP-RNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-11T07:34:41.913720Z",
+     "start_time": "2020-04-11T07:33:39.200Z"
+    }
+   },
+   "outputs": [],
    "source": [
     "trial, trainer, model = run_trial(\n",
     "    name=\"anp-rnn\",\n",
@@ -500,12 +566,18 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-04-11T05:31:34.200Z"
+     "end_time": "2020-04-11T07:34:41.915176Z",
+     "start_time": "2020-04-11T07:33:39.200Z"
     }
    },
    "outputs": [],
    "source": [
-    "%debug"
+    "name = trainer.logger.name\n",
+    "results[f\"{name}_{trial.number}\"]=dict(\n",
+    "    **trainer.logger.metrics[-1],\n",
+    "    **(trainer.logger.metrics[-2] if len(trainer.logger.metrics)>1 else {})\n",
+    ")\n",
+    "results[f\"{name}_{trial.number}\"]"
    ]
   },
   {
@@ -513,26 +585,12 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-04-11T05:31:34.200Z"
+     "end_time": "2020-04-11T05:46:41.864057Z",
+     "start_time": "2020-04-11T05:46:41.787999Z"
     }
    },
    "outputs": [],
-   "source": [
-    "results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2020-04-11T05:31:34.200Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "trainer.logger.metrics"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -551,7 +609,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-04-11T05:31:34.300Z"
+     "end_time": "2020-04-11T07:34:41.916492Z",
+     "start_time": "2020-04-11T07:33:39.200Z"
     }
    },
    "outputs": [],
@@ -574,12 +633,19 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-15T07:01:21.281138Z",
-     "start_time": "2020-02-15T07:01:20.834255Z"
+     "end_time": "2020-04-11T07:34:41.918079Z",
+     "start_time": "2020-04-11T07:33:39.200Z"
     }
    },
    "outputs": [],
-   "source": []
+   "source": [
+    "name = trainer.logger.name\n",
+    "results[f\"{name}_{trial.number}\"]=dict(\n",
+    "    **trainer.logger.metrics[-1],\n",
+    "    **(trainer.logger.metrics[-2] if len(trainer.logger.metrics)>1 else {})\n",
+    ")\n",
+    "results[f\"{name}_{trial.number}\"]"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -599,7 +665,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-04-11T05:31:34.300Z"
+     "end_time": "2020-04-11T07:34:41.919374Z",
+     "start_time": "2020-04-11T07:33:39.200Z"
     }
    },
    "outputs": [],
@@ -616,6 +683,25 @@
     "                                  PL_MODEL_CLS=PL_NeuralProcess)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-11T07:34:41.920622Z",
+     "start_time": "2020-04-11T07:33:39.200Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "name = trainer.logger.name\n",
+    "results[f\"{name}_{trial.number}\"]=dict(\n",
+    "    **trainer.logger.metrics[-1],\n",
+    "    **(trainer.logger.metrics[-2] if len(trainer.logger.metrics)>1 else {})\n",
+    ")\n",
+    "results[f\"{name}_{trial.number}\"]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -628,7 +714,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-04-11T05:31:34.300Z"
+     "end_time": "2020-04-11T07:34:41.921916Z",
+     "start_time": "2020-04-11T07:33:39.200Z"
     }
    },
    "outputs": [],
@@ -645,6 +732,26 @@
     "                                  PL_MODEL_CLS=PL_NeuralProcess)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-11T07:34:41.923211Z",
+     "start_time": "2020-04-11T07:33:39.200Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "name = trainer.logger.name\n",
+    "results[f\"{name}_{trial.number}\"]=dict(\n",
+    "    **trainer.logger.metrics[-1],\n",
+    "    **(trainer.logger.metrics[-2] if len(trainer.logger.metrics)>1 else {})\n",
+    ")\n",
+    "results[f\"{name}_{trial.number}\"]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -662,23 +769,52 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-04-11T05:31:34.300Z"
+     "end_time": "2020-04-11T07:34:41.924517Z",
+     "start_time": "2020-04-11T07:33:39.200Z"
     }
    },
    "outputs": [],
    "source": [
     "trial, trainer, model = run_trial(name=\"PL_Transformer\",\n",
     "                                  params={\n",
-    "                                      'det_enc_cross_attn_type': 'uniform',\n",
-    "                                      'det_enc_self_attn_type': 'uniform',\n",
-    "                                      'latent_enc_self_attn_type': 'uniform',\n",
-    "                                      'use_deterministic_path': False,\n",
-    "                                      'use_lvar': False,\n",
     "                                  },\n",
-    "                                  user_attrs=default_user_attrs,\n",
+    "                                  user_attrs={**default_user_attrs, 'context_in_target': False},\n",
     "                                  PL_MODEL_CLS=PL_Transformer)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-11T07:34:41.925801Z",
+     "start_time": "2020-04-11T07:33:39.200Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# %debug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-11T07:34:41.927063Z",
+     "start_time": "2020-04-11T07:33:39.300Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "name = trainer.logger.name\n",
+    "results[f\"{name}_{trial.number}\"]=dict(\n",
+    "    **trainer.logger.metrics[-1],\n",
+    "    **(trainer.logger.metrics[-2] if len(trainer.logger.metrics)>1 else {})\n",
+    ")\n",
+    "results[f\"{name}_{trial.number}\"]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -691,23 +827,38 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-04-11T05:31:34.300Z"
+     "end_time": "2020-04-11T07:34:41.928494Z",
+     "start_time": "2020-04-11T07:33:39.300Z"
     }
    },
    "outputs": [],
    "source": [
     "trial, trainer, model = run_trial(name=\"TransformerSeq2Seq_PL\",\n",
-    "                                  params={\n",
-    "                                      'det_enc_cross_attn_type': 'uniform',\n",
-    "                                      'det_enc_self_attn_type': 'uniform',\n",
-    "                                      'latent_enc_self_attn_type': 'uniform',\n",
-    "                                      'use_deterministic_path': False,\n",
-    "                                      'use_lvar': False,\n",
-    "                                  },\n",
+    "                                  params={},\n",
     "                                  user_attrs=default_user_attrs,\n",
     "                                  PL_MODEL_CLS=TransformerSeq2Seq_PL)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-11T07:34:41.929842Z",
+     "start_time": "2020-04-11T07:33:39.300Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "name = trainer.logger.name\n",
+    "results[f\"{name}_{trial.number}\"]=dict(\n",
+    "    **trainer.logger.metrics[-1],\n",
+    "    **(trainer.logger.metrics[-2] if len(trainer.logger.metrics)>1 else {})\n",
+    ")\n",
+    "results[f\"{name}_{trial.number}\"]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -720,23 +871,38 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-04-11T05:31:34.300Z"
-    }
+     "end_time": "2020-04-11T07:34:41.931210Z",
+     "start_time": "2020-04-11T07:33:39.300Z"
+    },
+    "scrolled": true
    },
    "outputs": [],
    "source": [
     "trial, trainer, model = run_trial(name=\"LSTM_PL_STD\",\n",
-    "                                  params={\n",
-    "                                      'det_enc_cross_attn_type': 'uniform',\n",
-    "                                      'det_enc_self_attn_type': 'uniform',\n",
-    "                                      'latent_enc_self_attn_type': 'uniform',\n",
-    "                                      'use_deterministic_path': False,\n",
-    "                                      'use_lvar': False,\n",
-    "                                  },\n",
+    "                                  params={},\n",
     "                                  user_attrs=default_user_attrs,\n",
     "                                  PL_MODEL_CLS=LSTM_PL_STD)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-11T07:34:41.932472Z",
+     "start_time": "2020-04-11T07:33:39.300Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "name = trainer.logger.name\n",
+    "results[f\"{name}_{trial.number}\"]=dict(\n",
+    "    **trainer.logger.metrics[-1],\n",
+    "    **(trainer.logger.metrics[-2] if len(trainer.logger.metrics)>1 else {})\n",
+    ")\n",
+    "results[f\"{name}_{trial.number}\"]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -749,23 +915,37 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-04-11T05:31:34.300Z"
+     "end_time": "2020-04-11T07:34:41.934047Z",
+     "start_time": "2020-04-11T07:33:39.300Z"
     }
    },
    "outputs": [],
    "source": [
     "trial, trainer, model = run_trial(name=\"LSTMSeq2Seq_PL\",\n",
-    "                                  params={\n",
-    "                                      'det_enc_cross_attn_type': 'uniform',\n",
-    "                                      'det_enc_self_attn_type': 'uniform',\n",
-    "                                      'latent_enc_self_attn_type': 'uniform',\n",
-    "                                      'use_deterministic_path': False,\n",
-    "                                      'use_lvar': False,\n",
-    "                                  },\n",
+    "                                  params={},\n",
     "                                  user_attrs=default_user_attrs,\n",
     "                                  PL_MODEL_CLS=LSTMSeq2Seq_PL)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-04-11T07:34:41.935335Z",
+     "start_time": "2020-04-11T07:33:39.300Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "name = trainer.logger.name\n",
+    "results[f\"{name}_{trial.number}\"]=dict(\n",
+    "    **trainer.logger.metrics[-1],\n",
+    "    **(trainer.logger.metrics[-2] if len(trainer.logger.metrics)>1 else {})\n",
+    ")\n",
+    "results[f\"{name}_{trial.number}\"]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -783,7 +963,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-04-11T05:31:34.300Z"
+     "end_time": "2020-04-11T07:34:41.936575Z",
+     "start_time": "2020-04-11T07:33:39.300Z"
     },
     "scrolled": true
    },
@@ -808,7 +989,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-04-11T05:31:34.300Z"
+     "end_time": "2020-04-11T07:34:41.937868Z",
+     "start_time": "2020-04-11T07:33:39.300Z"
     },
     "scrolled": true
    },
@@ -834,7 +1016,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-04-11T05:31:34.300Z"
+     "end_time": "2020-04-11T07:34:41.939222Z",
+     "start_time": "2020-04-11T07:33:39.300Z"
     },
     "scrolled": true
    },
@@ -886,7 +1069,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-04-11T05:31:34.300Z"
+     "end_time": "2020-04-11T07:34:41.940518Z",
+     "start_time": "2020-04-11T07:33:39.300Z"
     }
    },
    "outputs": [],
@@ -900,7 +1084,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2020-04-11T05:31:34.300Z"
+     "end_time": "2020-04-11T07:34:41.941905Z",
+     "start_time": "2020-04-11T07:33:39.300Z"
     }
    },
    "outputs": [],