diff --git a/src/triacontagon/loop.py b/src/triacontagon/loop.py
index e5f96bf..870c07e 100644
--- a/src/triacontagon/loop.py
+++ b/src/triacontagon/loop.py
@@ -1,6 +1,6 @@
 from .model import Model, \
     TrainingBatch
-from .batch import Batcher
+from .batch import DualBatcher
 from .sampling import negative_sample_data
 from .data import Data
 import torch
diff --git a/src/triacontagon/sampling.py b/src/triacontagon/sampling.py
index bcec040..ab033dd 100644
--- a/src/triacontagon/sampling.py
+++ b/src/triacontagon/sampling.py
@@ -14,6 +14,60 @@ from .data import Data, \
     EdgeType
 from .cumcount import cumcount
 import time
+import multiprocessing
+import multiprocessing.pool
+from itertools import product, \
+    repeat
+from functools import reduce
+
+
+def fixed_unigram_candidate_sampler_slow(
+    true_classes: torch.Tensor,
+    num_repeats: torch.Tensor,
+    unigrams: torch.Tensor,
+    distortion: float = 1.) -> torch.Tensor:
+
+    assert isinstance(true_classes, torch.Tensor)
+    assert isinstance(num_repeats, torch.Tensor)
+    assert isinstance(unigrams, torch.Tensor)
+    distortion = float(distortion)
+
+    if len(true_classes.shape) != 2:
+        raise ValueError('true_classes must be a 2D matrix with shape (num_samples, num_true)')
+
+    if len(num_repeats.shape) != 1:
+        raise ValueError('num_repeats must be 1D')
+
+    if torch.any((unigrams > 0).sum() - \
+        (true_classes >= 0).sum(dim=1) < \
+            num_repeats):
+        raise ValueError('Not enough classes to choose from')
+
+    res = []
+
+    if distortion != 1.:
+        unigrams = unigrams.to(torch.float64)
+        unigrams = unigrams ** distortion
+
+    def fun(i):
+        if i and i % 100 == 0:
+            print(i)
+        if num_repeats[i] == 0:
+            return []
+        pos = torch.flatten(true_classes[i, :])
+        pos = pos[pos >= 0]
+        w = unigrams.clone().detach()
+        w[pos] = 0
+        sampler = torch.utils.data.WeightedRandomSampler(w,
+            num_repeats[i].item(), replacement=False)
+        res = list(sampler)
+        return res
+
+    with multiprocessing.pool.ThreadPool() as p:
+        res = p.map(fun, range(len(num_repeats)))
+    res = reduce(list.__add__, res, [])
+
+    return torch.tensor(res)
 
 
 def fixed_unigram_candidate_sampler(
@@ -61,6 +115,12 @@ def fixed_unigram_candidate_sampler(
         print('result:', result)
         mask = (candidates == true_classes[indices[:, 1], :])
         mask = mask.sum(1).to(torch.bool)
+        # append_true_classes = torch.full(( len(true_classes), ), -1)
+        # append_true_classes[~mask] = torch.flatten(candidates)[~mask]
+        # true_classes = torch.cat([
+        #     append_true_classes.view(-1, 1),
+        #     true_classes
+        # ], dim=1)
         print('mask:', mask)
         indices = indices[mask]
         # result[indices] = 0