docstr: Overrides initial optimizer config for specific parameters.
  
  The key-values of the optimizer config for the input parameters are overridden
  This can be both, optimizer parameters like "betas", or "lr" or it can be
  8-bit specific parameters like "optim_bits", "percentile_clipping".
  
  Parameters
  ----------
  parameters : torch.Tensor or list(torch.Tensors)
      The input parameters.
  key : str
      The hyperparamter to override.
  value : object
      The value for the hyperparamters.
  key_value_dict : dict
      A dictionary with multiple key-values to override.
code: def override_config(
      self, parameters, key=None, value=None, key_value_dict=None
  ):
      """
      Overrides initial optimizer config for specific parameters.
  
      The key-values of the optimizer config for the input parameters are overridden
      This can be both, optimizer parameters like "betas", or "lr" or it can be
      8-bit specific parameters like "optim_bits", "percentile_clipping".
  
      Parameters
      ----------
      parameters : torch.Tensor or list(torch.Tensors)
          The input parameters.
      key : str
          The hyperparamter to override.
      value : object
          The value for the hyperparamters.
      key_value_dict : dict
          A dictionary with multiple key-values to override.
      """
      self.uses_config_override = True
      if isinstance(parameters, torch.nn.Parameter):
          parameters = [parameters]
      if isinstance(parameters, torch.Tensor):
          parameters = [parameters]
      if key is not None and value is not None:
          assert key_value_dict is None
          key_value_dict = {key: value}
  
      if key_value_dict is not None:
          for p in parameters:
              if id(p) in self.pid2config:
                  self.pid2config[id(p)].update(key_value_dict)
              else:
                  self.pid2config[id(p)] = key_value_dict
  
imports: from collections import abc
  from collections import defaultdict
  from copy import deepcopy
  from itertools import chain
  import torch
  import bitsandbytes.functional
label_desc: Use image node_id 5 for calling the GlobalOptimManager obj's underlying member method code with example usage: obj.override_config(parameters, key, value, key_value_dict) without return types