docstr: Computes trend based on model configuration.
  
  Parameters
  ----------
      t : torch.Tensor float
          normalized time, dim: (batch, n_forecasts)
      meta: dict
          Metadata about the all the samples of the model input batch. Contains the following:
              * ``df_name`` (list, str), time series ID corresponding to each sample of the input batch.
  Returns
  -------
      torch.Tensor
          Trend component, same dimensions as input t
docstr_tok: ["Computes","trend","based","on","model","configuration",".","Parameters","--","--","--","--","--","t",":","torch.Tensor","float","normalized","time",",","dim",":","(","batch",",","n_forecasts",")","meta",":","dict","Metadata","about","the","all","the","samples","of","the","model","input","batch",".","Contains","the","following",":","*","``","df_name","``","(","list",",","str",")",",","time","series","ID","corresponding","to","each","sample","of","the","input","batch",".","Returns","--","--","--","-","torch.Tensor","Trend","component",",","same","dimensions","as","input","t"]
code: def forward(self, t, meta):
      """
      Computes trend based on model configuration.
  
      Parameters
      ----------
          t : torch.Tensor float
              normalized time, dim: (batch, n_forecasts)
          meta: dict
              Metadata about the all the samples of the model input batch. Contains the following:
                  * ``df_name`` (list, str), time series ID corresponding to each sample of the input batch.
      Returns
      -------
          torch.Tensor
              Trend component, same dimensions as input t
      """
      # From the dataloader meta data, we get the one-hot encoding of the df_name.
      meta_name_tensor_one_hot = nn.functional.one_hot(
          meta, num_classes=len(self.id_list)
      )
      # trend_k_0 = trend_k_0(sample metadata)
      # dimensions - batch_size, segments(1), quantiles
      trend_k_0 = torch.sum(
          meta_name_tensor_one_hot.unsqueeze(dim=0).unsqueeze(dim=-1)
          * self.trend_k0.unsqueeze(dim=1),
          dim=2,
      ).permute(1, 2, 0)
      # dimensions -  batch_size, n_forecasts, quantiles
      trend = trend_k_0 * t.unsqueeze(2)
      return self.bias.unsqueeze(dim=0).unsqueeze(dim=0) + trend
  
file_path: neural_prophet/neuralprophet/components/trend/linear.py
imports: import torch
  import torch.nn
  from neuralprophet.components.trend import Trend
  from neuralprophet.utils_torch import init_parameter
label_desc: Use image node_id 2 for calling the LocalLinearTrend obj's underlying member method code with example usage: obj.forward(t, meta) and returns: unknown