code: def get_scheduler(
      name: Union[str, SchedulerType],
      optimizer: Optimizer,
      num_warmup_steps: Optional[int] = None,
      num_training_steps: Optional[int] = None,
      scheduler_specific_kwargs: Optional[dict] = None,
  ):
      """
      Unified API to get any scheduler from its name.
  
      Args:
          name (`str` or `SchedulerType`):
              The name of the scheduler to use.
          optimizer (`torch.optim.Optimizer`):
              The optimizer that will be used during training.
          num_warmup_steps (`int`, *optional*):
              The number of warmup steps to do. This is not required by all schedulers (hence the argument being
              optional), the function will raise an error if it's unset and the scheduler type requires it.
          num_training_steps (`int``, *optional*):
              The number of training steps to do. This is not required by all schedulers (hence the argument being
              optional), the function will raise an error if it's unset and the scheduler type requires it.
          scheduler_specific_kwargs (`dict`, *optional*):
              Extra parameters for schedulers such as cosine with restarts. Mismatched scheduler types and scheduler
              parameters will cause the scheduler function to raise a TypeError.
      """
      name = SchedulerType(name)
      schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
      if name == SchedulerType.CONSTANT:
          return schedule_func(optimizer)
  
      if scheduler_specific_kwargs is None:
          scheduler_specific_kwargs = {}
  
      if name == SchedulerType.REDUCE_ON_PLATEAU:
          return schedule_func(optimizer, **scheduler_specific_kwargs)
  
      # All other schedulers require `num_warmup_steps`
      if num_warmup_steps is None:
          raise ValueError(
              f"{name} requires `num_warmup_steps`, please provide that argument."
          )
  
      if name == SchedulerType.CONSTANT_WITH_WARMUP:
          return schedule_func(
              optimizer, num_warmup_steps=num_warmup_steps
          )
  
      if name == SchedulerType.INVERSE_SQRT:
          return schedule_func(
              optimizer, num_warmup_steps=num_warmup_steps
          )
  
      # All other schedulers require `num_training_steps`
      if num_training_steps is None:
          raise ValueError(
              f"{name} requires `num_training_steps`, please provide that argument."
          )
  
      return schedule_func(
          optimizer,
          num_warmup_steps=num_warmup_steps,
          num_training_steps=num_training_steps,
          **scheduler_specific_kwargs,
      )
  
imports: import math
  import warnings
  from functools import partial
  from typing import Callable, Iterable, Optional, Tuple, Union
  import torch
  from torch import nn
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import LambdaLR, ReduceLROnPlateau
  from .trainer_utils import SchedulerType
  from .utils import logging
  from .utils.versions import require_version
label_desc: Use image node_id 16 for calling a global function with example usage: get_scheduler(name, optimizer, num_warmup_steps, num_training_steps, scheduler_specific_kwargs) and returns: schedule_func, schedule_func, schedule_func, schedule_func, schedule_func