class_docstr: Args:
      params (iterable): iterable of parameters to optimize or dicts defining
          parameter groups
      lr (float): learning rate (required)
class_docstr_tok: ["Args",":","params","(","iterable",")",":","iterable","of","parameters","to","optimize","or","dicts","defining","parameter","groups","lr","(","float",")",":","learning","rate","(","required",")"]
code: def __init__(self, params, lr=0.01):
      defaults = dict(lr=lr)
      super(MySGD, self).__init__(params, defaults)
  
imports: import torch
  from torch import nn
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader, Dataset
  from fairscale.experimental.nn.ampnet_pipe.pipe import AMPnetPipe
  from fairscale.fair_dev.testing.testing import get_worker_map, torch_spawn
label_desc: Use image node_id 1 to create a new MySGD object from inherited base classes: Optimizer with example: obj = MySGD(params, lr)