class_docstr: This creates a configurable optimizer.
  
  Arguments:
      learning_rate (float): Learning rate of the optimizer. Defaults to ``0.01``.
      momentum (float): Momentum of the optimizer. Defaults to ``0.5``.
      parameters: Model parameters to optimize.
      d_model (int): Embedding size of the model (for Noam optimizer).
      betas (Tuple[float, float]): Betas for Adam optimizer. Defaults to ``(0.9, 0.999)``.
      eps (float): Epsilon for Adam/RMSProp optimizers. Defaults to ``1e-8``.
      step_factor (int): Step factor for Noam optimizer. Defaults to ``1024``.
  
  `Here's an example usage <https://github.com/labmlai/labml/blob/master/samples/pytorch/mnist/e_labml_helpers.py>`_.
  
  Also there is a better (more options) implementation in ``labml_nn``.
  `We recommend using that <https://nn.labml.ai/optimizers/configs.html>`_.
code: def __init__(self):
      super().__init__(_primary="optimizer")
  
file_path: labml/helpers/labml_helpers/optimizer.py
imports: from typing import Tuple
  import torch
  from labml import tracker
  from labml.configs import BaseConfigs, option, meta_config
label_desc: Use image node_id 1 to create a new OptimizerConfigs object from inherited base classes: BaseConfigs with example: obj = OptimizerConfigs()