class_docstr: Linear (constant) warm-up followed by cosine learning rate annealing
class_docstr_tok: ["Linear","(","constant",")","warm-up","followed","by","cosine","learning","rate","annealing"]
code: def _get_closed_form_lr(self):
      cosine_lr = [
          self.eta_min
          + (base_lr - self.eta_min)
          * (
              1
              + math.cos(
                  math.pi
                  * (self.last_epoch - self.ramp_len)  # type: ignore
                  / (self.T_max - self.ramp_len - 1)
              )
          )
          / 2
          for base_lr in self.base_lrs  # type: ignore
      ]
      return (
          self.base_lrs
          if self.last_epoch < self.ramp_len
          else cosine_lr
      )
  
file_path: NeuralCompression/projects/torch_vct/utils/schedulers.py
imports: import math
  import torch
label_desc: Use image node_id 3 for calling the LinearWarmUpCosineLR obj's underlying member method code with example usage: obj._get_closed_form_lr() and returns: unknown