Nhits weight argument fix in TimeSeriesDataSet

pytorch_forecasting/metrics/point.py

@@ -203,7 +203,7 @@ def update(
 
         # weight samples
         if weight is not None:
-            losses = losses * weight.unsqueeze(-1)
+            losses = losses * weight
 
         self._update_losses_and_lengths(losses, lengths)
 

tests/test_models/conftest.py

@@ -130,6 +130,36 @@ def make_dataloaders(data_with_covariates, **kwargs):
 def multiple_dataloaders_with_covariates(data_with_covariates, request):
     return make_dataloaders(data_with_covariates, **request.param)
 
+@pytest.fixture(scope="session")
+def dataloaders_with_different_encoder_decoder_length(data_with_covariates):
+    return make_dataloaders(
+        data_with_covariates.copy(),
+        target="target",
+        time_varying_known_categoricals=["special_days", "month"],
+        variable_groups=dict(
+            special_days=[
+                "easter_day",
+                "good_friday",
+                "new_year",
+                "christmas",
+                "labor_day",
+                "independence_day",
+                "revolution_day_memorial",
+                "regional_games",
+                "fifa_u_17_world_cup",
+                "football_gold_cup",
+                "beer_capital",
+                "music_fest",
+            ]
+        ),
+        time_varying_known_reals=["time_idx", "price_regular", "price_actual", "discount", "discount_in_percent"],
+        time_varying_unknown_categoricals=[],
+        time_varying_unknown_reals=["target", "volume", "log_volume", "industry_volume", "soda_volume", "avg_max_temp"],
+        static_categoricals=["agency"],
+        add_relative_time_idx=False,
+        target_normalizer=GroupNormalizer(groups=["agency", "sku"], center=False),
+    )
+
 
 @pytest.fixture(scope="session")
 def dataloaders_with_different_encoder_decoder_length(data_with_covariates):