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
      """
      return self.bias.unsqueeze(dim=0).repeat(
          t.shape[0], t.shape[1], 1
      )
  
imports: from neuralprophet.components.trend import Trend
label_desc: Use image node_id 2 for calling the StaticTrend obj's underlying member method code with example usage: obj.forward(t, meta) and returns: self