class_docstr: ## Base class for *Adam* and extensions
docstr: ### Initialize state for a given parameter tensor
  
  This should be overridden with code to initialize `state` for parameters `param`.
  `group` is the parameter group dictionary to which `param` belongs.
docstr_tok: ["#","#","#","Initialize","state","for","a","given","parameter","tensor","This","should","be","overridden","with","code","to","initialize","`","state","`","for","parameters","`","param","`",".","`","group","`","is","the","parameter","group","dictionary","to","which","`","param","`","belongs","."]
code: def init_state(
      self,
      state: Dict[str, any],
      group: Dict[str, any],
      param: nn.Parameter,
  ):
      """
      ### Initialize state for a given parameter tensor
  
      This should be overridden with code to initialize `state` for parameters `param`.
      `group` is the parameter group dictionary to which `param` belongs.
      """
      pass
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/optimizers/__init__.py
imports: from typing import Dict, Tuple, Any
  import torch
  from torch import nn
  from torch.optim.optimizer import Optimizer
label_desc: Use image node_id 2 for calling the GenericAdaptiveOptimizer obj's underlying member method code with example usage: obj.init_state(state, group, param) without return types