prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <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?
  
  </issue>
  <code>
  [start of neuralprophet/components/trend/static.py]
  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 of neuralprophet/components/trend/static.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: 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):
  
verification_info: {"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"}]}