class_docstr: Implements a row-wise variant of the Adagrad algorithm.
  Assumes that all the model parameters are 2-dimensional tensors
  containing embedding weights.
  
  Code mostly copy-pasted from torch/optim/Adagrad, with HOGWILD-safe
  update (see async_adagrad.py)
code: def share_memory(self):
      for group in self.param_groups:
          for p in group["params"]:
              state = self.state[p]
              state["sum"].share_memory_()
  
imports: import logging
  from torch.optim import Optimizer
label_desc: Use image node_id 2 for calling the RowAdagrad obj's underlying member method code with example usage: obj.share_memory() without return types