class_docstr: A differentiable version of the Adagrad optimizer.
  
  This optimizer creates a gradient tape as it updates parameters.
class_docstr_tok: ["A","differentiable","version","of","the","Adagrad","optimizer",".","This","optimizer","creates","a","gradient","tape","as","it","updates","parameters","."]
code: def _update(self, grouped_grads: _GroupedGradsType, **kwargs) -> None:
      zipped = zip(self.param_groups, grouped_grads)
      for group_idx, (group, grads) in enumerate(zipped):
          for p_idx, (p, g) in enumerate(zip(group["params"], grads)):
              if g is None:
                  continue
  
              state = self.state[group_idx][p_idx]
  
              state["step"] += 1
  
              if group["weight_decay"] != 0:
                  if g.data.is_sparse:
                      raise RuntimeError(
                          "weight_decay option is not compatible with sparse "
                          "gradients"
                      )
                  g = _add(g, group["weight_decay"], p)
  
              clr = group["lr"] / (
                  1 + (state["step"] - 1) * group["lr_decay"]
              )
  
              if g.is_sparse:
                  # TODO: implement support for sparse gradients.
                  raise NotImplementedError(
                      "sparse gradient support for DifferentiableAdagrad not "
                      "implemented yet."
                  )
              else:
                  state["sum"] = sum_ = _addcmul(state["sum"], 1, g, g)
                  mask = sum_ == 0.0
                  _maybe_mask(sum_, mask)
                  std = _add(
                      state["sum"].sqrt(),
                      group["eps"] if "eps" in group else 1e-10,
                  )
                  group["params"][p_idx] = _addcdiv(p, -clr, g, std)
  
imports: import abc
  import collections
  import copy
  import math
  import typing
  import warnings
  import torch
  from .None import patch
  from .None import utils
label_desc: Use image node_id 1 for calling the DifferentiableAdagrad obj's underlying member method code with example usage: obj._update(grouped_grads) without return types