code: def __new__(cls, *args, **kwargs):
      # Import locally to avoid circular imports.
      from keras.backend.torch.optimizers import torch_adadelta
      from keras.backend.torch.optimizers import torch_adagrad
      from keras.backend.torch.optimizers import torch_adam
      from keras.backend.torch.optimizers import torch_adamax
      from keras.backend.torch.optimizers import torch_adamw
      from keras.backend.torch.optimizers import torch_lion
      from keras.backend.torch.optimizers import torch_nadam
      from keras.backend.torch.optimizers import torch_rmsprop
      from keras.backend.torch.optimizers import torch_sgd
  
      OPTIMIZERS = {
          optimizers.Adadelta: torch_adadelta.Adadelta,
          optimizers.Adagrad: torch_adagrad.Adagrad,
          optimizers.Adam: torch_adam.Adam,
          optimizers.Adamax: torch_adamax.Adamax,
          optimizers.AdamW: torch_adamw.AdamW,
          optimizers.Lion: torch_lion.Lion,
          optimizers.Nadam: torch_nadam.Nadam,
          optimizers.RMSprop: torch_rmsprop.RMSprop,
          optimizers.SGD: torch_sgd.SGD,
      }
  
      if cls in OPTIMIZERS:
          return OPTIMIZERS[cls](*args, **kwargs)
      return super().__new__(cls)
  
file_path: keras/keras/backend/torch/optimizers/torch_optimizer.py
imports: import torch
  from keras import optimizers
  from keras.optimizers.base_optimizer import BaseOptimizer
  from keras.utils import torch_utils
label_desc: Use image node_id 1 for calling the TorchOptimizer obj's underlying member method code with example usage: obj.__new__(cls) and returns: super, OPTIMIZERS