text: """
  The lightning training loop handles everything except the actual computations of your model.
   To decide what will happen in your training loop, define the `training_step` function.
  
  Below are all the things lightning automates for you in the training loop.
  
  Accumulated gradients
  ---------------------
  
  Accumulated gradients runs K small batches of size N before doing a backwards pass.
   The effect is a large effective batch size of size KxN.
  
  .. code-block:: python
  
      # DEFAULT (ie: no accumulated grads)
      trainer = Trainer(accumulate_grad_batches=1)
  
  Force training for min or max epochs
  ------------------------------------
  
  It can be useful to force training for a minimum number of epochs or limit to a max number
  
  .. code-block:: python
  
      # DEFAULT
      trainer = Trainer(min_epochs=1, max_epochs=1000)
  
  Force disable early stop
  ------------------------
  
  To disable early stopping pass None to the early_stop_callback
  
  .. code-block:: python
  
      # DEFAULT
      trainer = Trainer(early_stop_callback=None)
  
  Gradient Clipping
  -----------------
  
  Gradient clipping may be enabled to avoid exploding gradients.
   Specifically, this will `clip the gradient norm computed over all model parameters
   `together <https://pytorch.org/docs/stable/nn.html#torch.nn.utils.clip_grad_norm_>`_.
  
  .. code-block:: python
  
      # DEFAULT (ie: don't clip)
      trainer = Trainer(gradient_clip_val=0)
  
      # clip gradients with norm above 0.5
      trainer = Trainer(gradient_clip_val=0.5)
  
  Inspect gradient norms
  ----------------------
  
  Looking at grad norms can help you figure out where training might be going wrong.
  
  .. code-block:: python
  
      # DEFAULT (-1 doesn't track norms)
      trainer = Trainer(track_grad_norm=-1)
  
      # track the LP norm (P=2 here)
      trainer = Trainer(track_grad_norm=2)
  
  Set how much of the training set to check
  -----------------------------------------
  
  If you don't want to check 100% of the training set (for debugging or if it's huge), set this flag.
  
  train_percent_check will be overwritten by overfit_pct if `overfit_pct > 0`
  
  .. code-block:: python
  
      # DEFAULT
      trainer = Trainer(train_percent_check=1.0)
  
      # check 10% only
      trainer = Trainer(train_percent_check=0.1)
  
  Packed sequences as inputs
  --------------------------
  
  When using PackedSequence, do 2 things:
  1. return either a padded tensor in dataset or a list of variable length tensors
  in the dataloader collate_fn (example above shows the list implementation).
  2. Pack the sequence in forward or training and validation steps depending on use case.
  
  .. code-block:: python
  
      # For use in dataloader
      def collate_fn(batch):
          x = [item[0] for item in batch]
          y = [item[1] for item in batch]
          return x, y
  
      # In module
      def training_step(self, batch, batch_idx):
          x = rnn.pack_sequence(batch[0], enforce_sorted=False)
          y = rnn.pack_sequence(batch[1], enforce_sorted=False)
  
  
  Truncated Backpropagation Through Time
  --------------------------------------
  
  There are times when multiple backwards passes are needed for each batch.
   For example, it may save memory to use Truncated Backpropagation Through Time when training RNNs.
  
  When this flag is enabled each batch is split into sequences of size truncated_bptt_steps
   and passed to training_step(...) separately. A default splitting function is provided,
   however, you can override it for more flexibility. See `tbptt_split_batch`.
  
  .. code-block:: python
  
      # DEFAULT (single backwards pass per batch)
      trainer = Trainer(truncated_bptt_steps=None)
  
      # (split batch into sequences of size 2)
      trainer = Trainer(truncated_bptt_steps=2)
  
  
  NaN detection and intervention
  ------------------------------
  When the `terminate_on_nan` flag is enabled, after every forward pass during training, Lightning will
  check that
  
  1. the loss you return in `training_step` is finite (not NaN and not +/-inf)
  2. the model parameters have finite values.
  
  Lightning will terminate the training loop with an error message if NaN or infinite
  values are detected. If this happens, you should investigate numerically unstable operations
  in your model.
  
  .. code-block:: python
  
      # DEFAULT (won't perform the NaN check)
      trainer = Trainer(terminate_on_nan=False)
  
      # (NaN check each batch and terminate on NaN or infinite values)
      trainer = Trainer(terminate_on_nan=True)
  
  """
  
  from abc import ABC, abstractmethod
  from typing import Callable
  from typing import Union, List
  
  import numpy as np
  from torch.utils.data import DataLoader
  import torch
  
  from pytorch_lightning import _logger as log
  from pytorch_lightning.callbacks.base import Callback
  from pytorch_lightning.core.lightning import LightningModule
  from pytorch_lightning.loggers import LightningLoggerBase
  from pytorch_lightning.utilities.exceptions import MisconfigurationException
  from pytorch_lightning.trainer.supporters import TensorRunningAccum
  from pytorch_lightning.utilities import rank_zero_warn
  
  try:
      from apex import amp
  except ImportError:
      APEX_AVAILABLE = False
  else:
      APEX_AVAILABLE = True
  
  try:
      import torch_xla.distributed.parallel_loader as xla_pl
      import torch_xla.core.xla_model as xm
  except ImportError:
      XLA_AVAILABLE = False
  else:
      XLA_AVAILABLE = True
  
  try:
      import horovod.torch as hvd
  except ImportError:
      HOROVOD_AVAILABLE = False
  else:
      HOROVOD_AVAILABLE = True
  
  
  class TrainerTrainLoopMixin(ABC):
  
      # this is just a summary on variables used in this abstract class,
      #  the proper values/initialisation should be done in child class
      max_epochs: int
      min_epochs: int
      on_gpu: bool
      use_ddp: bool
      use_dp: bool
      use_ddp2: bool
      use_horovod: bool
      single_gpu: bool
      use_tpu: bool
      data_parallel_device_ids: ...
      check_val_every_n_epoch: ...
      num_training_batches: int
      val_check_batch: ...
      num_val_batches: int
      disable_validation: bool
      fast_dev_run: ...
      accumulation_scheduler: ...
      lr_schedulers: ...
      enable_early_stop: ...
      early_stop_callback: ...
      callback_metrics: ...
      logger: Union[LightningLoggerBase, bool]
      global_step: int
      testing: bool
      log_save_interval: float
      proc_rank: int
      row_log_interval: float
      truncated_bptt_steps: ...
      optimizers: ...
      optimizer_frequencies: ...
      accumulate_grad_batches: int
      track_grad_norm: ...
      model: LightningModule
      interrupted: bool
      running_loss: ...
      progress_bar_dict: ...
      reduce_lr_on_plateau_scheduler: ...
      profiler: ...
      batch_idx: int
      precision: ...
      train_dataloader: DataLoader
      reload_dataloaders_every_epoch: bool
      max_steps: int
      min_steps: int
      total_batch_idx: int
      checkpoint_callback: ...
      terminate_on_nan: bool
      tpu_id: int
  
      # Callback system
      callbacks: List[Callback]
      on_train_start: Callable
      on_train_end: Callable
      on_batch_start: Callable
      on_batch_end: Callable
      on_epoch_start: Callable
      on_epoch_end: Callable
      on_validation_end: Callable
  
      @abstractmethod
      def get_model(self):
          """Warning: this is just empty shell for code implemented in other class."""
  
      @abstractmethod
      def is_function_implemented(self, *args):
          """Warning: this is just empty shell for code implemented in other class."""
  
      @abstractmethod
      def run_evaluation(self, *args):
          """Warning: this is just empty shell for code implemented in other class."""
  
      @abstractmethod
      def transfer_batch_to_gpu(self, *args):
          """Warning: this is just empty shell for code implemented in other class."""
  
      @abstractmethod
      def transfer_batch_to_tpu(self, *args):
          """Warning: this is just empty shell for code implemented in other class."""
  
      @abstractmethod
      def clip_gradients(self):
          """Warning: this is just empty shell for code implemented in other class."""
  
      @abstractmethod
      def detect_nan_tensors(self, *args):
          """Warning: this is just empty shell for code implemented in other class."""
  
      @abstractmethod
      def is_overridden(self, *args):
          """Warning: this is just empty shell for code implemented in other class."""
  
      @abstractmethod
      def add_progress_bar_metrics(self, *args):
          """Warning: this is just empty shell for code implemented in other class."""
  
      @abstractmethod
      def log_metrics(self, *args):
          """Warning: this is just empty shell for code implemented in other class."""
  
      @abstractmethod
      def process_output(self, *args):
          """Warning: this is just empty shell for code implemented in other class."""
  
      @abstractmethod
      def reset_train_dataloader(self, *args):
          """Warning: this is just empty shell for code implemented in other class."""
  
      @abstractmethod
      def reset_val_dataloader(self, model):
          """Warning: this is just empty shell for code implemented in other class."""
  
      @abstractmethod
      def has_arg(self, *args):
          """Warning: this is just empty shell for code implemented in other class."""
  
      def train(self):
          # get model
          model = self.get_model()
  
          # load data
          # if reload_dataloaders_every_epoch, this is moved to the epoch loop
          if not self.reload_dataloaders_every_epoch:
              self.reset_train_dataloader(model)
          self.reset_val_dataloader(model)
  
          # Train start events
          with self.profiler.profile('on_train_start'):
              # callbacks
              self.on_train_start()
              # initialize early stop callback
              if self.early_stop_callback is not None:
                  self.early_stop_callback.on_train_start(self, self.get_model())
              # model hooks
              model.on_train_start()
  
          try:
              # run all epochs
              for epoch in range(self.current_epoch, self.max_epochs):
                  # reset train dataloader
                  if self.reload_dataloaders_every_epoch:
                      self.reset_train_dataloader(model)
                  # set seed for distributed sampler (enables shuffling for each epoch)
                  if (self.use_ddp or self.use_horovod) \
                          and hasattr(self.train_dataloader.sampler, 'set_epoch'):
                      self.train_dataloader.sampler.set_epoch(epoch)
  
                  # update training progress in trainer and model
                  model.current_epoch = epoch
                  self.current_epoch = epoch
  
                  # changing gradient according accumulation_scheduler
                  self.accumulation_scheduler.on_epoch_start(self, self.get_model())
  
                  # stores accumulated grad fractions per batch
                  self.batch_loss_value = TensorRunningAccum(
                      window_length=self.accumulate_grad_batches
                  )
  
                  # -----------------
                  # RUN TNG EPOCH
                  # -----------------
                  self.run_training_epoch()
  
                  if self.max_steps and self.max_steps == self.global_step:
                      self.run_training_teardown()
                      return
  
                  # update LR schedulers
                  self.update_learning_rates(interval='epoch')
  
                  # early stopping
                  met_min_epochs = epoch >= self.min_epochs - 1
                  met_min_steps = self.global_step >= self.min_steps if self.min_steps else True
  
                  # TODO wrap this logic into the callback
                  if self.enable_early_stop:
                      if (met_min_epochs and met_min_steps) or self.fast_dev_run:
                          should_stop = self.early_stop_callback.on_epoch_end(self, self.get_model())
                          # stop training
                          stop = should_stop and met_min_epochs
                          if stop:
                              self.run_training_teardown()
                              return
  
              self.run_training_teardown()
  
          except KeyboardInterrupt:
              if self.proc_rank == 0:
                  log.info('Detected KeyboardInterrupt, attempting graceful shutdown...')
              self.interrupted = True
              self.run_training_teardown()
  
      def run_training_epoch(self):
  
          # get model
          model = self.get_model()
  
          # Epoch start events
          with self.profiler.profile('on_epoch_start'):
              # callbacks
              self.on_epoch_start()
  
              # model hooks
              if self.is_function_implemented('on_epoch_start'):
                  model.on_epoch_start()
  
          # track local dataloader so TPU can wrap each epoch
          train_dataloader = self.train_dataloader
  
          # on TPU we have to wrap it under the ParallelLoader
          if self.use_tpu and self.tpu_id is None:
              device = xm.xla_device()
              train_dataloader = xla_pl.ParallelLoader(train_dataloader, [device])
              train_dataloader = train_dataloader.per_device_loader(device)
  
          # bookkeeping
          outputs = []
  
          # run epoch
          for batch_idx, (batch, is_last_batch) in self.profiler.profile_iterable(
              enumerate(_with_is_last(train_dataloader)), "get_train_batch"
          ):
              # stop epoch if we limited the number of training batches
              if batch_idx >= self.num_training_batches:
                  break
  
              self.batch_idx = batch_idx
  
              model.global_step = self.global_step
  
              # ---------------
              # RUN TRAIN STEP
              # ---------------
              _outputs = self.run_training_batch(batch, batch_idx)
              batch_result, grad_norm_dic, batch_step_metrics, batch_output = _outputs
  
              # only track outputs when user implements training_epoch_end
              # otherwise we will build up unnecessary memory
              if self.is_overridden('training_epoch_end', model=self.get_model()):
                  outputs.append(batch_output)
  
              # when returning -1 from train_step, we end epoch early
              early_stop_epoch = batch_result == -1
  
              # TODO: consolidate all actions that need to take place only after
              # self.accumulate_grad_batches steps (optimizer step, lr update, global step increment)
              if (self.batch_idx + 1) % self.accumulate_grad_batches == 0:
                  # update lr
                  self.update_learning_rates(interval='step')
  
              # ---------------
              # RUN VAL STEP
              # ---------------
              is_val_check_batch = (batch_idx + 1) % self.val_check_batch == 0
              can_check_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0
              can_check_val = not self.disable_validation and can_check_epoch
              should_check_val = is_val_check_batch or early_stop_epoch
              should_check_val = should_check_val or (is_last_batch and self.val_check_batch == float('inf'))
              should_check_val = can_check_val and should_check_val
  
              # ---------------
              # CHECKPOINTING, EARLY STOPPING
              # ---------------
              # fast_dev_run always forces val checking after train batch
              if self.fast_dev_run or should_check_val:
                  self.run_evaluation(test_mode=self.testing)
                  self.call_checkpoint_callback()
                  self.call_early_stop_callback()
  
              # when logs should be saved
              should_save_log = (batch_idx + 1) % self.log_save_interval == 0 or early_stop_epoch
              if should_save_log or self.fast_dev_run:
                  if self.proc_rank == 0 and self.logger is not None:
                      self.logger.save()
  
              # when metrics should be logged
              should_log_metrics = batch_idx % self.row_log_interval == 0 or early_stop_epoch
              if should_log_metrics or self.fast_dev_run:
                  # logs user requested information to logger
                  self.log_metrics(batch_step_metrics, grad_norm_dic)
  
              # progress global step according to grads progress
              if (self.batch_idx + 1) % self.accumulate_grad_batches == 0:
                  self.global_step += 1
              self.total_batch_idx += 1
  
              # max steps reached, end training
              if self.max_steps is not None and self.max_steps == self.global_step:
                  break
  
              # end epoch early
              # stop when the flag is changed or we've gone past the amount
              # requested in the batches
              if early_stop_epoch or self.fast_dev_run:
                  break
  
          if self.use_horovod:
              hvd.join(hvd.local_rank() if self.on_gpu else -1)
  
          # process epoch outputs
          model = self.get_model()
          if self.is_overridden('training_epoch_end', model=model):
              epoch_output = model.training_epoch_end(outputs)
              _processed_outputs = self.process_output(epoch_output)
              log_epoch_metrics = _processed_outputs[2]
              callback_epoch_metrics = _processed_outputs[3]
              self.log_metrics(log_epoch_metrics, {})
              self.callback_metrics.update(callback_epoch_metrics)
              self.add_progress_bar_metrics(_processed_outputs[1])
  
          # when no val loop is present or fast-dev-run still need to call checkpoints
          if not self.is_overridden('validation_step') and not (self.fast_dev_run or should_check_val):
              self.call_checkpoint_callback()
              self.call_early_stop_callback()
  
          # Epoch end events
          with self.profiler.profile('on_epoch_end'):
              # callbacks
              self.on_epoch_end()
              # model hooks
              if self.is_function_implemented('on_epoch_end'):
                  model.on_epoch_end()
  
      def run_training_batch(self, batch, batch_idx):
          # track grad norms
          grad_norm_dic = {}
  
          # track all metrics for callbacks
          all_callback_metrics = []
  
          # track metrics to log
          all_log_metrics = []
  
          if batch is None:
              return 0, grad_norm_dic, {}, {}
  
          # Batch start events
          with self.profiler.profile('on_batch_start'):
              # callbacks
              self.on_batch_start()
              # hooks
              if self.is_function_implemented('on_batch_start'):
                  response = self.get_model().on_batch_start(batch)
                  if response == -1:
                      return -1, grad_norm_dic, {}, {}
  
          splits = [batch]
          if self.truncated_bptt_steps is not None:
              model_ref = self.get_model()
              with self.profiler.profile('tbptt_split_batch'):
                  splits = model_ref.tbptt_split_batch(batch, self.truncated_bptt_steps)
  
          self.hiddens = None
          for split_idx, split_batch in enumerate(splits):
              self.split_idx = split_idx
  
              for opt_idx, optimizer in self._get_optimizers_iterable():
                  # make sure only the gradients of the current optimizer's parameters are calculated
                  # in the training step to prevent dangling gradients in multiple-optimizer setup.
                  if len(self.optimizers) > 1:
                      for param in self.get_model().parameters():
                          param.requires_grad = False
                      for group in optimizer.param_groups:
                          for param in group['params']:
                              param.requires_grad = True
  
                  # wrap the forward step in a closure so second order methods work
                  def optimizer_closure():
                      # forward pass
                      with self.profiler.profile('model_forward'):
                          if self.use_amp and self.use_native_amp:
                              with torch.cuda.amp.autocast():
                                  output_dict = self.training_forward(split_batch, batch_idx,
                                                                      opt_idx, self.hiddens)
                          else:
                              output_dict = self.training_forward(split_batch, batch_idx, opt_idx, self.hiddens)
  
                          # format and reduce outputs accordingly
                          processed_output = self.process_output(output_dict, train=True)
  
                      closure_loss, progress_bar_metrics, log_metrics, callback_metrics, self.hiddens = processed_output
  
                      # accumulate loss
                      # (if accumulate_grad_batches = 1 no effect)
                      closure_loss = closure_loss / self.accumulate_grad_batches
  
                      # backward pass
                      model_ref = self.get_model()
                      with self.profiler.profile('model_backward'):
                          model_ref.backward(self, closure_loss, optimizer, opt_idx)
  
                      # track metrics for callbacks
                      all_callback_metrics.append(callback_metrics)
  
                      # track progress bar metrics
                      self.add_progress_bar_metrics(progress_bar_metrics)
                      all_log_metrics.append(log_metrics)
  
                      if self.use_horovod:
                          # Synchronize Horovod to ensure gradient manipulations (e.g., loss scaling) are valid
                          optimizer.synchronize()
  
                      # insert after step hook
                      if self.is_function_implemented('on_after_backward'):
                          model_ref = self.get_model()
                          with self.profiler.profile('on_after_backward'):
                              model_ref.on_after_backward()
  
                      return closure_loss, callback_metrics
  
                  # calculate loss
                  loss, batch_output = optimizer_closure()
  
                  # check if loss or model weights are nan
                  if self.terminate_on_nan:
                      self.detect_nan_tensors(loss)
  
                  # track total loss for logging (avoid mem leaks)
                  self.batch_loss_value.append(loss)
  
                  # gradient update with accumulated gradients
                  if (self.batch_idx + 1) % self.accumulate_grad_batches == 0:
  
                      # track gradient norms when requested
                      if batch_idx % self.row_log_interval == 0:
                          if self.track_grad_norm > 0:
                              model = self.get_model()
                              grad_norm_dic = model.grad_norm(
                                  self.track_grad_norm)
  
                      # clip gradients
                      if self.use_amp and self.use_native_amp:
                          self.scaler.unscale_(optimizer)
                      self.clip_gradients()
  
                      # calls .step(), .zero_grad()
                      # override function to modify this behavior
                      model = self.get_model()
                      with self.profiler.profile('optimizer_step'):
                          model.optimizer_step(self.current_epoch, batch_idx,
                                               optimizer, opt_idx,
                                               lambda: optimizer_closure()[0])
  
                      # calculate running loss for display
                      self.running_loss.append(self.batch_loss_value.mean())
  
                      # reset for next set of accumulated grads
                      self.batch_loss_value.reset()
  
          # Batch end events
          with self.profiler.profile('on_batch_end'):
              # callbacks
              self.on_batch_end()
              # model hooks
              if self.is_function_implemented('on_batch_end'):
                  self.get_model().on_batch_end()
  
          # collapse all metrics into one dict
          all_log_metrics = {k: v for d in all_log_metrics for k, v in d.items()}
  
          # track all metrics for callbacks
          self.callback_metrics.update({k: v for d in all_callback_metrics for k, v in d.items()})
  
          return 0, grad_norm_dic, all_log_metrics, batch_output
  
      def _get_optimizers_iterable(self):
          if not self.optimizer_frequencies:
              # call training_step once per optimizer
              return list(enumerate(self.optimizers))
  
          optimizer_freq_cumsum = np.cumsum(self.optimizer_frequencies)
          optimizers_loop_length = optimizer_freq_cumsum[-1]
          current_place_in_loop = self.total_batch_idx % optimizers_loop_length
  
          # find optimzier index by looking for the first {item > current_place} in the cumsum list
          opt_idx = np.argmax(optimizer_freq_cumsum > current_place_in_loop)
          return [(opt_idx, self.optimizers[opt_idx])]
  
      def run_training_teardown(self):
          # Train end events
          with self.profiler.profile('on_train_end'):
              # callbacks
              self.on_train_end()
              # model hooks
              if self.is_function_implemented('on_train_end'):
                  self.get_model().on_train_end()
  
          if self.logger is not None:
              self.logger.finalize("success")
  
          # summarize profile results
          self.profiler.describe()
  
      def training_forward(self, batch, batch_idx, opt_idx, hiddens):
          """
          Handle forward for each training case (distributed, single gpu, etc...)
          :param batch:
          :param batch_idx:
          :return:
          """
          # ---------------
          # FORWARD
          # ---------------
          # enable not needing to add opt_idx to training_step
          args = [batch, batch_idx]
  
          if len(self.optimizers) > 1:
              if self.has_arg('training_step', 'optimizer_idx'):
                  args.append(opt_idx)
              else:
                  num_opts = len(self.optimizers)
                  raise ValueError(
                      f'Your LightningModule defines {num_opts} optimizers but '
                      f'training_step is missing the "optimizer_idx" argument.'
                  )
  
          # pass hiddens if using tbptt
          if self.truncated_bptt_steps is not None:
              args.append(hiddens)
  
          # distributed forward
          if self.use_ddp or self.use_ddp2 or self.use_dp:
              output = self.model(*args)
  
          # Horovod
          elif self.use_horovod and self.on_gpu:
              batch = self.transfer_batch_to_gpu(batch, hvd.local_rank())
              args[0] = batch
              output = self.model.training_step(*args)
  
          # single GPU forward
          elif self.single_gpu:
              gpu_id = 0
              if isinstance(self.data_parallel_device_ids, list):
                  gpu_id = self.data_parallel_device_ids[0]
  
              # Don't copy the batch since there is a single gpu that the batch could
              # be referenced from and if there are multiple optimizers the batch will
              # wind up copying it to the same device repeatedly.
              batch = self.transfer_batch_to_gpu(batch, gpu_id)
              args[0] = batch
              output = self.model.training_step(*args)
  
          # TPU support
          elif self.use_tpu:
              batch = self.transfer_batch_to_tpu(batch)
              args[0] = batch
              output = self.model.training_step(*args)
  
          # CPU forward
          else:
              output = self.model.training_step(*args)
  
          # allow any mode to define training_step_end
          # do something will all the dp outputs (like softmax)
          if self.is_overridden('training_step_end'):
              model_ref = self.get_model()
              with self.profiler.profile('training_step_end'):
                  output = model_ref.training_step_end(output)
  
          # allow any mode to define training_end
          # TODO: remove in 1.0.0
          if self.is_overridden('training_end'):
              model_ref = self.get_model()
              with self.profiler.profile('training_end'):
                  output = model_ref.training_end(output)
  
              rank_zero_warn('`training_end` was deprecated in 0.7.0 and will be removed 1.0.0.'
                             ' Use training_epoch_end instead', DeprecationWarning)
  
          return output
  
      def update_learning_rates(self, interval: str):
          """Update learning rates.
  
          Args:
              interval: either 'epoch' or 'step'.
          """
          if not self.lr_schedulers:
              return
  
          for lr_scheduler in self.lr_schedulers:
              current_idx = self.batch_idx if interval == 'step' else self.current_epoch
              current_idx += 1  # account for both batch and epoch starts from 0
              # Take step if call to update_learning_rates matches the interval key and
              # the current step modulo the schedulers frequency is zero
              if lr_scheduler['interval'] == interval and current_idx % lr_scheduler['frequency'] == 0:
                  # If instance of ReduceLROnPlateau, we need to pass validation loss
                  if lr_scheduler['reduce_on_plateau']:
                      monitor_key = lr_scheduler['monitor']
                      monitor_val = self.callback_metrics.get(monitor_key)
                      if monitor_val is None:
                          avail_metrics = ','.join(list(self.callback_metrics.keys()))
                          raise MisconfigurationException(
                              f'ReduceLROnPlateau conditioned on metric {monitor_key}'
                              f' which is not available. Available metrics are: {avail_metrics}.'
                              ' Condition can be set using `monitor` key in lr scheduler dict'
                          )
                      lr_scheduler['scheduler'].step(monitor_val)
                  else:
                      lr_scheduler['scheduler'].step()
  
      def call_checkpoint_callback(self):
          if self.checkpoint_callback is not None:
              self.checkpoint_callback.on_validation_end(self, self.get_model())
  
      def call_early_stop_callback(self):
          if self.early_stop_callback:
              self.early_stop_callback.on_epoch_end(self, self.get_model())
  
  
  def _with_is_last(iterable):
      """Pass through values from the given iterable with an added boolean indicating if this is the last item.
      See `https://stackoverflow.com/a/1630350 <https://stackoverflow.com/a/1630350>`_"""
      it = iter(iterable)
      last = next(it)
      for val in it:
          # yield last and has next
          yield last, False
          last = val
      # yield last, no longer has next
      yield last, True
  