docstr: This method computes the trend component of the model.
  
  
  Parameters
  ----------
      t : torch.Tensor, float
          time
  
      k_t : torch.Tensor, int
          see compute_k_t
  
      m_t : torch.Tensor, bool
          see compute_m_t
  
      meta_name_tensor_one_hot : torch.Tensor, float
          Metadata about the all the samples of the model input batch.
  
          Contains the following:
              * ``df_name`` (list, str), time series name ID corresponding to each sample of the input batch.
  Returns
  -------
      torch.Tensor
          trend_component,  ( batch_size, n_forecasts, quantiles_size)
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code: def compute_trend(self, t, k_t, m_t, meta_name_tensor_one_hot=None):
      """This method computes the trend component of the model.
  
  
      Parameters
      ----------
          t : torch.Tensor, float
              time
  
          k_t : torch.Tensor, int
              see compute_k_t
  
          m_t : torch.Tensor, bool
              see compute_m_t
  
          meta_name_tensor_one_hot : torch.Tensor, float
              Metadata about the all the samples of the model input batch.
  
              Contains the following:
                  * ``df_name`` (list, str), time series name ID corresponding to each sample of the input batch.
      Returns
      -------
          torch.Tensor
              trend_component,  ( batch_size, n_forecasts, quantiles_size)
      """
      pass
  
imports: import numpy
  import torch
  import torch.nn
  from neuralprophet.components.trend import Trend
  from neuralprophet.utils_torch import init_parameter
label_desc: Use image node_id 7 for calling the PiecewiseLinearTrend obj's underlying member method code with example usage: obj.compute_trend(t, k_t, m_t, meta_name_tensor_one_hot) without return types