text: from typing import Any, Callable, Optional, TYPE_CHECKING
  
  import torch
  from torch.optim import Optimizer
  
  from pytorch_lightning.accelerators.accelerator import Accelerator
  from pytorch_lightning.plugins.precision import MixedPrecisionPlugin
  from pytorch_lightning.plugins.training_type.single_tpu import SingleTPUPlugin
  from pytorch_lightning.plugins.training_type.tpu_spawn import TPUSpawnPlugin
  from pytorch_lightning.utilities import _XLA_AVAILABLE
  from pytorch_lightning.utilities.exceptions import MisconfigurationException
  
  if _XLA_AVAILABLE:
      import torch_xla.core.xla_model as xm
  
  if TYPE_CHECKING:
      from pytorch_lightning.core.lightning import LightningModule
      from pytorch_lightning.trainer.trainer import Trainer
  
  
  class TPUAccelerator(Accelerator):
  
      def setup(self, trainer: 'Trainer', model: 'LightningModule') -> None:
          """
          Raises:
              MisconfigurationException:
                  If AMP is used with TPU, or if TPUs are not using a single TPU core or TPU spawn training.
          """
          if isinstance(self.precision_plugin, MixedPrecisionPlugin):
              raise MisconfigurationException(
                  "amp + tpu is not supported. "
                  "Only bfloats are supported on TPU. Consider using TPUHalfPrecisionPlugin"
              )
  
          if not isinstance(self.training_type_plugin, (SingleTPUPlugin, TPUSpawnPlugin)):
              raise MisconfigurationException("TPUs only support a single tpu core or tpu spawn training.")
          return super().setup(trainer, model)
  
      def run_optimizer_step(
          self, optimizer: Optimizer, optimizer_idx: int, lambda_closure: Callable, **kwargs: Any
      ) -> None:
          xm.optimizer_step(optimizer, barrier=False, optimizer_args={'closure': lambda_closure, **kwargs})
  
      def all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> torch.Tensor:
          """
          Function to gather a tensor from several distributed processes
          Args:
              tensor: tensor of shape (batch, ...)
              group: the process group to gather results from. Defaults to all processes (world)
              sync_grads: flag that allows users to synchronize gradients for all_gather op
          Return:
              A tensor of shape (world_size, batch, ...)
          """
          # todo: Add support for backward with all_gather
          if torch.distributed.is_initialized():
              return xm.all_gather(tensor, group=group, sync_grads=sync_grads)
          return tensor
  