We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Static Trend does not follow required forecasting pattern
**Source Code:**

```
import pandas as pd
from neuralprophet import NeuralProphet, set_log_level, set_random_seed
set_log_level("ERROR")

data_location = "https://raw.githubusercontent.com/ourownstory/neuralprophet-data/main/datasets/"
file = "energy/SF_hospital_load.csv"
data_df = pd.read_csv(data_location + file)

m = NeuralProphet()
train_df, test_df = m.split_df(data_df, freq="H", valid_p=1.0 / 5)

quantile_lo, quantile_hi = 0.05, 0.95
quantiles = [quantile_lo, quantile_hi]
n_lags = 3 * 24
n_forecasts = 24

m = NeuralProphet(
    growth="off",
    yearly_seasonality=False,
    weekly_seasonality=False,
    daily_seasonality=False,
    n_forecasts = n_forecasts,
    n_lags=n_lags,
    ar_reg=1,
    learning_rate=0.01,
    quantiles=quantiles,
)

random_seed = 0
set_random_seed(random_seed)
metrics = m.fit(train_df, freq="H")

forecast = m.predict(test_df)
```

**Error:**

```
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Input In [1], in <cell line: 34>()
     31 metrics = m.fit(train_df, freq="H")
     32 metrics.tail(1)
---> 34 forecast = m.predict(test_df)

File ~.../neuralprophet/forecaster.py:940, in NeuralProphet.predict(self, df, decompose, raw)
    938         fcst = fcst[:-1]
    939 else:
--> 940     fcst = self._reshape_raw_predictions_to_forecst_df(df_i, predicted, components)
    941     if periods_added[df_name] > 0:
    942         fcst = fcst[: -periods_added[df_name]]

File ~.../neuralprophet/forecaster.py:3169, in NeuralProphet._reshape_raw_predictions_to_forecst_df(self, df, predicted, components)
   3167 for j in range(len(self.config_train.quantiles)):
   3168     forecast_0 = components[comp][0, :, j]
-> 3169     forecast_rest = components[comp][1:, self.n_forecasts - 1, j]
   3170     yhat = np.concatenate(([np.NaN] * self.max_lags, forecast_0, forecast_rest))
   3171     if j == 0:  # temporary condition to add only the median component
   3172         # add yhat into dataframe, using df_forecast indexing

IndexError: index 23 is out of bounds for axis 1 with size 1
```

Is it because I used the empty `m = NeuralProphet()` to split to data to train/test?

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `neuralprophet/components/trend/static.py`
Content:
```
1 from neuralprophet.components.trend import Trend
2 
3 
4 class StaticTrend(Trend):
5     def __init__(self, config, id_list, quantiles, num_trends_modelled, n_forecasts, device):
6         super().__init__(
7             config=config,
8             n_forecasts=n_forecasts,
9             num_trends_modelled=num_trends_modelled,
10             quantiles=quantiles,
11             id_list=id_list,
12             device=device,
13         )
14 
15     def forward(self, t, meta):
16         """
17         Computes trend based on model configuration.
18 
19         Parameters
20         ----------
21             t : torch.Tensor float
22                 normalized time, dim: (batch, n_forecasts)
23             meta: dict
24                 Metadata about the all the samples of the model input batch. Contains the following:
25                     * ``df_name`` (list, str), time series ID corresponding to each sample of the input batch.
26         Returns
27         -------
28             torch.Tensor
29                 Trend component, same dimensions as input t
30         """
31         return self.bias.unsqueeze(dim=0).repeat(t.shape[0], 1, 1)
32 
33     @property
34     def get_trend_deltas(self):
35         pass
36 
37     def add_regularization(self):
38         pass
39
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/neuralprophet/components/trend/static.py b/neuralprophet/components/trend/static.py
--- a/neuralprophet/components/trend/static.py
+++ b/neuralprophet/components/trend/static.py
@@ -28,7 +28,7 @@
             torch.Tensor
                 Trend component, same dimensions as input t
         """
-        return self.bias.unsqueeze(dim=0).repeat(t.shape[0], 1, 1)
+        return self.bias.unsqueeze(dim=0).repeat(t.shape[0], self.n_forecasts, 1)
 
     @property
     def get_trend_deltas(self):

{"golden_diff": "diff --git a/neuralprophet/components/trend/static.py b/neuralprophet/components/trend/static.py\n--- a/neuralprophet/components/trend/static.py\n+++ b/neuralprophet/components/trend/static.py\n@@ -28,7 +28,7 @@\n             torch.Tensor\n                 Trend component, same dimensions as input t\n         \"\"\"\n-        return self.bias.unsqueeze(dim=0).repeat(t.shape[0], 1, 1)\n+        return self.bias.unsqueeze(dim=0).repeat(t.shape[0], self.n_forecasts, 1)\n \n     @property\n     def get_trend_deltas(self):\n", "issue": "Static Trend does not follow required forecasting pattern\n**Source Code:**\r\n\r\n```\r\nimport pandas as pd\r\nfrom neuralprophet import NeuralProphet, set_log_level, set_random_seed\r\nset_log_level(\"ERROR\")\r\n\r\ndata_location = \"https://raw.githubusercontent.com/ourownstory/neuralprophet-data/main/datasets/\"\r\nfile = \"energy/SF_hospital_load.csv\"\r\ndata_df = pd.read_csv(data_location + file)\r\n\r\nm = NeuralProphet()\r\ntrain_df, test_df = m.split_df(data_df, freq=\"H\", valid_p=1.0 / 5)\r\n\r\nquantile_lo, quantile_hi = 0.05, 0.95\r\nquantiles = [quantile_lo, quantile_hi]\r\nn_lags = 3 * 24\r\nn_forecasts = 24\r\n\r\nm = NeuralProphet(\r\n    growth=\"off\",\r\n    yearly_seasonality=False,\r\n    weekly_seasonality=False,\r\n    daily_seasonality=False,\r\n    n_forecasts = n_forecasts,\r\n    n_lags=n_lags,\r\n    ar_reg=1,\r\n    learning_rate=0.01,\r\n    quantiles=quantiles,\r\n)\r\n\r\nrandom_seed = 0\r\nset_random_seed(random_seed)\r\nmetrics = m.fit(train_df, freq=\"H\")\r\n\r\nforecast = m.predict(test_df)\r\n```\r\n\r\n**Error:**\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nIndexError                                Traceback (most recent call last)\r\nInput In [1], in <cell line: 34>()\r\n     31 metrics = m.fit(train_df, freq=\"H\")\r\n     32 metrics.tail(1)\r\n---> 34 forecast = m.predict(test_df)\r\n\r\nFile ~.../neuralprophet/forecaster.py:940, in NeuralProphet.predict(self, df, decompose, raw)\r\n    938         fcst = fcst[:-1]\r\n    939 else:\r\n--> 940     fcst = self._reshape_raw_predictions_to_forecst_df(df_i, predicted, components)\r\n    941     if periods_added[df_name] > 0:\r\n    942         fcst = fcst[: -periods_added[df_name]]\r\n\r\nFile ~.../neuralprophet/forecaster.py:3169, in NeuralProphet._reshape_raw_predictions_to_forecst_df(self, df, predicted, components)\r\n   3167 for j in range(len(self.config_train.quantiles)):\r\n   3168     forecast_0 = components[comp][0, :, j]\r\n-> 3169     forecast_rest = components[comp][1:, self.n_forecasts - 1, j]\r\n   3170     yhat = np.concatenate(([np.NaN] * self.max_lags, forecast_0, forecast_rest))\r\n   3171     if j == 0:  # temporary condition to add only the median component\r\n   3172         # add yhat into dataframe, using df_forecast indexing\r\n\r\nIndexError: index 23 is out of bounds for axis 1 with size 1\r\n```\r\n\r\nIs it because I used the empty `m = NeuralProphet()` to split to data to train/test?\n", "before_files": [{"content": "from neuralprophet.components.trend import Trend\n\n\nclass StaticTrend(Trend):\n    def __init__(self, config, id_list, quantiles, num_trends_modelled, n_forecasts, device):\n        super().__init__(\n            config=config,\n            n_forecasts=n_forecasts,\n            num_trends_modelled=num_trends_modelled,\n            quantiles=quantiles,\n            id_list=id_list,\n            device=device,\n        )\n\n    def forward(self, t, meta):\n        \"\"\"\n        Computes trend based on model configuration.\n\n        Parameters\n        ----------\n            t : torch.Tensor float\n                normalized time, dim: (batch, n_forecasts)\n            meta: dict\n                Metadata about the all the samples of the model input batch. Contains the following:\n                    * ``df_name`` (list, str), time series ID corresponding to each sample of the input batch.\n        Returns\n        -------\n            torch.Tensor\n                Trend component, same dimensions as input t\n        \"\"\"\n        return self.bias.unsqueeze(dim=0).repeat(t.shape[0], 1, 1)\n\n    @property\n    def get_trend_deltas(self):\n        pass\n\n    def add_regularization(self):\n        pass\n", "path": "neuralprophet/components/trend/static.py"}], "after_files": [{"content": "from neuralprophet.components.trend import Trend\n\n\nclass StaticTrend(Trend):\n    def __init__(self, config, id_list, quantiles, num_trends_modelled, n_forecasts, device):\n        super().__init__(\n            config=config,\n            n_forecasts=n_forecasts,\n            num_trends_modelled=num_trends_modelled,\n            quantiles=quantiles,\n            id_list=id_list,\n            device=device,\n        )\n\n    def forward(self, t, meta):\n        \"\"\"\n        Computes trend based on model configuration.\n\n        Parameters\n        ----------\n            t : torch.Tensor float\n                normalized time, dim: (batch, n_forecasts)\n            meta: dict\n                Metadata about the all the samples of the model input batch. Contains the following:\n                    * ``df_name`` (list, str), time series ID corresponding to each sample of the input batch.\n        Returns\n        -------\n            torch.Tensor\n                Trend component, same dimensions as input t\n        \"\"\"\n        return self.bias.unsqueeze(dim=0).repeat(t.shape[0], self.n_forecasts, 1)\n\n    @property\n    def get_trend_deltas(self):\n        pass\n\n    def add_regularization(self):\n        pass\n", "path": "neuralprophet/components/trend/static.py"}]}