class_docstr: Set the learning rate of each parameter group using a cosine annealing
  schedule, where :math:`\eta_{max}` is set to the initial lr and
  :math:`T_{cur}` is the number of epochs since the last restart in SGDR:
  .. math::
      \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 +
      \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right)
      T_{cur} \neq (2k+1)T_{max};\\
      \eta_{t+1} = \eta_{t} + (\eta_{max} - \eta_{min})\frac{1 -
      \cos(\frac{1}{T_{max}}\pi)}{2},
      T_{cur} = (2k+1)T_{max}.\\
  When last_epoch=-1, sets initial lr as lr. Notice that because the schedule
  is defined recursively, the learning rate can be simultaneously modified
  outside this scheduler by other operators. If the learning rate is set
  solely by this scheduler, the learning rate at each step becomes:
  .. math::
      \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 +
      \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right)
  It has been proposed in
  `SGDR: Stochastic Gradient Descent with Warm Restarts`_. Note that this only
  implements the cosine annealing part of SGDR, and not the restarts.
  Args:
      optimizer (Optimizer): Wrapped optimizer.
      T_max (int): Maximum number of iterations.
      eta_min (float): Minimum learning rate. Default: 0.
      last_epoch (int): The index of last epoch. Default: -1.
  .. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
      https://arxiv.org/abs/1608.03983
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code: def _get_closed_form_lr(self):
      return [
          self.eta_min
          + (base_lr - self.eta_min)
          * (
              1
              + math.cos(
                  math.pi
                  * max(0, self.last_epoch - self.T_flat)
                  / max(0, self.T_max - self.T_flat)
              )
          )
          / 2
          for base_lr in self.base_lrs
      ]
  
imports: import math
  import warnings
  import numpy
  from torch import nn
  from torch.optim.optimizer import Optimizer
  from torch.optim.lr_scheduler import _LRScheduler, LambdaLR, ExponentialLR, CyclicLR, MultiStepLR, ReduceLROnPlateau, CosineAnnealingLR, CosineAnnealingWarmRestarts
label_desc: Use image node_id 3 for calling the FlatCosineAnnealingLR obj's underlying member method code with example usage: obj._get_closed_form_lr() and returns: unknown