docstr: m_t represents the value at the origin(t=0) that we would need to have so that if we use (k_t + k_0) as
  slope, we reach the same value at time = chagepoint_start_of_segment_i as we would reach by following the
  segmented slope (having in each segment the slope trend_deltas(i) + k_0)
  
  Parameters
  ----------
      current_segment : torch.Tensor, int
          segment corresponding to time t (batch_size, n_forecasts, segments (+ 1))
  
      past_next_changepoint : torch.Tensor, bool
          whether the a changepoint >= time t (batch_size, n_forecasts, segments (+ 1))
  
      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
          m_t,  ( batch_size, n_forecasts, quantiles_size)
docstr_tok: ["m_t","represents","the","value","at","the","origin","(","t=0",")","that","we","would","need","to","have","so","that","if","we","use","(","k_t","+","k_0",")","as","slope",",","we","reach","the","same","value","at","time","=","chagepoint_start_of_segment_i","as","we","would","reach","by","following","the","segmented","slope","(","having","in","each","segment","the","slope","trend_deltas","(","i",")","+","k_0",")","Parameters","--","--","--","--","--","current_segment",":","torch.Tensor",",","int","segment","corresponding","to","time","t","(","batch_size",",","n_forecasts",",","segments","(","+","1",")",")","past_next_changepoint",":","torch.Tensor",",","bool","whether","the","a","changepoint",">","=","time","t","(","batch_size",",","n_forecasts",",","segments","(","+","1",")",")","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","m_t",",","(","batch_size",",","n_forecasts",",","quantiles_size",")"]
code: def compute_m_t(
      self,
      current_segment,
      past_next_changepoint,
      meta_name_tensor_one_hot,
  ):
      """m_t represents the value at the origin(t=0) that we would need to have so that if we use (k_t + k_0) as
      slope, we reach the same value at time = chagepoint_start_of_segment_i as we would reach by following the
      segmented slope (having in each segment the slope trend_deltas(i) + k_0)
  
      Parameters
      ----------
          current_segment : torch.Tensor, int
              segment corresponding to time t (batch_size, n_forecasts, segments (+ 1))
  
          past_next_changepoint : torch.Tensor, bool
              whether the a changepoint >= time t (batch_size, n_forecasts, segments (+ 1))
  
          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
              m_t,  ( 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 6 for calling the PiecewiseLinearTrend obj's underlying member method code with example usage: obj.compute_m_t(current_segment, past_next_changepoint, meta_name_tensor_one_hot) without return types