text: import os
  import warnings
  import collections
  from argparse import Namespace
  
  import torch
  import torch.distributed as dist
  
  from pytorch_lightning.core.decorators import data_loader
  from pytorch_lightning.core.grads import GradInformation
  from pytorch_lightning.core.hooks import ModelHooks
  from pytorch_lightning.core.memory import ModelSummary
  from pytorch_lightning.core.saving import ModelIO
  from pytorch_lightning.trainer.trainer_io import load_hparams_from_tags_csv
  import logging
  from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
  
  
  class LightningModule(GradInformation, ModelIO, ModelHooks):
      """
      A LightningModule has the following properties which you can access at any time
  
      **logger**
      A reference to the logger you passed into trainer.
      Passing a logger is optional. If you don't pass one in, Lightning will create one
       for you automatically. This logger saves logs to `/os.getcwd()/lightning_logs`::
  
          Trainer(logger=your_logger)
  
  
      Call it from anywhere in your LightningModule to add metrics, images, etc...
       whatever your logger supports.
  
      Here is an example using the TestTubeLogger (which is a wrapper
       on 'PyTorch SummaryWriter <https://pytorch.org/docs/stable/tensorboard.html>`_
       with versioned folder structure).
  
      .. code-block:: python
  
          # if logger is a tensorboard logger or TestTubeLogger
          self.logger.experiment.add_embedding(...)
          self.logger.experiment.log({'val_loss': 0.9})
          self.logger.experiment.add_scalars(...)
  
  
      **trainer**
      Last resort access to any state the trainer has.
       Changing certain properties here could affect your training run.
  
      .. code-block:: python
  
          self.trainer.optimizers
          self.trainer.current_epoch
          ...
  
      Debugging
      ---------
  
      The LightningModule also offers these tricks to help debug.
  
      **example_input_array**
  
      In the LightningModule init, you can set a dummy tensor for this property
      to get a print out of sizes coming into and out of every layer.
  
      .. code-block:: python
  
          def __init__(self):
              # put the dimensions of the first input to your system
              self.example_input_array = torch.rand(5, 28 * 28)
  
  
      """
  
      def __init__(self, *args, **kwargs):
          super(LightningModule, self).__init__(*args, **kwargs)
  
          #: Current dtype
          self.dtype = torch.FloatTensor
          self.exp_save_path = None
          #: The current epoch
          self.current_epoch = 0
          #: Total training batches seen across all epochs
          self.global_step = 0
          self.loaded_optimizer_states_dict = {}
          self.trainer = None
          self.logger = None
          self.example_input_array = None
  
          # track if gpu was requested for checkpointing
          #: True if your model is currently running on GPUs.
          #: Useful to set flags around the LightningModule for different CPU vs GPU behavior.
          self.on_gpu = False
          self.use_dp = False
          self.use_ddp = False
          self.use_ddp2 = False
          self.use_amp = False
  
      def forward(self, *args, **kwargs):
          """
          Expand model in into whatever you need.
          Also need to return the target
          :param x:
          :return:
          """
          raise NotImplementedError
  
      def training_step(self, *args, **kwargs):
          """return loss, dict with metrics for tqdm
  
          :param batch: The output of your dataloader. A tensor, tuple or list
          :param int batch_idx: Integer displaying which batch this is
          :return: dict with loss key and optional log, progress keys
           if implementing training_step, return whatever you need in that step:
              - loss -> tensor scalar [REQUIRED]
              - progress_bar -> Dict for progress bar display. Must have only tensors
              - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)
  
          In this step you'd normally do the forward pass and calculate the loss for a batch.
           You can also do fancier things like multiple forward passes or something specific to your model.
  
          Example
          -------
  
          .. code-block:: python
  
              def training_step(self, batch, batch_idx):
                  x, y, z = batch
  
                  # implement your own
                  out = self.forward(x)
                  loss = self.loss(out, x)
  
                  logger_logs = {'training_loss': loss} # optional (MUST ALL BE TENSORS)
  
                  # if using TestTubeLogger or TensorboardLogger you can nest scalars
                  logger_logs = {'losses': logger_logs} # optional (MUST ALL BE TENSORS)
  
                  output = {
                      'loss': loss, # required
                      'progress_bar': {'training_loss': loss}, # optional (MUST ALL BE TENSORS)
                      'log': logger_logs
                  }
  
                  # return a dict
                  return output
  
          If you define multiple optimizers, this step will also be called with an additional `optimizer_idx` param.
  
          .. code-block:: python
  
              # Multiple optimizers (ie: GANs)
              def training_step(self, batch, batch_idx, optimizer_idx):
                  if optimizer_idx == 0:
                      # do training_step with encoder
                  if optimizer_idx == 1:
                      # do training_step with decoder
  
  
          If you add truncated back propagation through time you will also get an additional
           argument with the hidden states of the previous step.
  
          .. code-block:: python
  
              # Truncated back-propagation through time
              def training_step(self, batch, batch_idx, hiddens):
                  # hiddens are the hiddens from the previous truncated backprop step
  
          You can also return a -1 instead of a dict to stop the current loop. This is useful
           if you want to break out of the current training epoch early.
          """
          raise NotImplementedError
  
      def training_end(self, *args, **kwargs):
          """return loss, dict with metrics for tqdm
  
          :param outputs: What you return in `training_step`.
          :return dict: dictionary with loss key and optional log, progress keys:
              - loss -> tensor scalar [REQUIRED]
              - progress_bar -> Dict for progress bar display. Must have only tensors
              - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)
  
          In certain cases (dp, ddp2), you might want to use all outputs of every process to do something.
          For instance, if using negative samples, you could run a batch via dp and use ALL the outputs
          for a single softmax across the full batch (ie: the denominator would use the full batch).
  
          In this case you should define training_end to perform those calculations.
  
          Example
          -------
  
          .. code-block:: python
  
              # WITHOUT training_end
              # if used in DP or DDP2, this batch is 1/num_gpus large
              def training_step(self, batch, batch_idx):
                  # batch is 1/num_gpus big
                  x, y = batch
  
                  out = self.forward(x)
                  loss = self.softmax(out)
                  loss = nce_loss(loss)
                  return {'loss': loss}
  
              # --------------
              # with training_end to do softmax over the full batch
              def training_step(self, batch, batch_idx):
                  # batch is 1/num_gpus big
                  x, y = batch
  
                  out = self.forward(x)
                  return {'out': out}
  
              def training_end(self, outputs):
                  # this out is now the full size of the batch
                  out = outputs['out']
  
                  # this softmax now uses the full batch size
                  loss = self.softmax(out)
                  loss = nce_loss(loss)
                  return {'loss': loss}
  
          If you define multiple optimizers, this step will also be called with an additional `optimizer_idx` param.
  
          .. code-block:: python
  
              # Multiple optimizers (ie: GANs)
              def training_step(self, batch, batch_idx, optimizer_idx):
                  if optimizer_idx == 0:
                      # do training_step with encoder
                  if optimizer_idx == 1:
                      # do training_step with decoder
  
          If you add truncated back propagation through time you will also get an additional argument
           with the hidden states of the previous step.
  
          .. code-block:: python
  
              # Truncated back-propagation through time
              def training_step(self, batch, batch_idx, hiddens):
                  # hiddens are the hiddens from the previous truncated backprop step
  
          You can also return a -1 instead of a dict to stop the current loop. This is useful if you want to
          break out of the current training epoch early.
          """
          pass
  
      def validation_step(self, *args, **kwargs):
          """return whatever outputs will need to be aggregated in validation_end
  
          :param batch: The output of your dataloader. A tensor, tuple or list
          :param int batch_idx: Integer displaying which batch this is
          :param int dataloader_idx: Integer displaying which dataloader this is (only if multiple val datasets used)
          :return dict: Dict or OrderedDict - passed to the validation_end step
  
          .. code-block:: python
  
              # if you have one val dataloader:
              def validation_step(self, batch, batch_idx)
  
              # if you have multiple val dataloaders:
              def validation_step(self, batch, batch_idx, dataloader_idxdx)
  
          If you don't need to validate you don't need to implement this method.
           In this step you'd normally generate examples or calculate anything of interest such as accuracy.
  
          When the validation_step is called, the model has been put in eval mode and PyTorch gradients
           have been disabled. At the end of validation, model goes back to training mode and gradients are enabled.
  
          The dict you return here will be available in the `validation_end` method.
  
          Example
          -------
  
          .. code-block:: python
  
              # CASE 1: A single validation dataset
              def validation_step(self, batch, batch_idx):
                  x, y = batch
  
                  # implement your own
                  out = self.forward(x)
                  loss = self.loss(out, y)
  
                  # log 6 example images
                  # or generated text... or whatever
                  sample_imgs = x[:6]
                  grid = torchvision.utils.make_grid(sample_imgs)
                  self.logger.experiment.add_image('example_images', grid, 0)
  
                  # calculate acc
                  labels_hat = torch.argmax(out, dim=1)
                  val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
  
                  # all optional...
                  # return whatever you need for the collation function validation_end
                  output = OrderedDict({
                      'val_loss': loss_val,
                      'val_acc': torch.tensor(val_acc), # everything must be a tensor
                  })
  
                  # return an optional dict
                  return output
  
          If you pass in multiple validation datasets, validation_step will have an additional argument.
  
          .. code-block:: python
  
              # CASE 2: multiple validation datasets
              def validation_step(self, batch, batch_idx, dataset_idx):
                  # dataset_idx tells you which dataset this is.
  
          The `dataset_idx` corresponds to the order of datasets returned in `val_dataloader`.
          """
          pass
  
      def test_step(self, *args, **kwargs):
          """return whatever outputs will need to be aggregated in test_end
  
          :param batch: The output of your dataloader. A tensor, tuple or list
          :param int batch_idx: Integer displaying which batch this is
          :param int dataloader_idx: Integer displaying which dataloader this is (only if multiple test datasets used)
          :return dict: Dict or OrderedDict with metrics to display in progress bar. All keys must be tensors.
  
          .. code-block:: python
  
              # if you have one test dataloader:
              def test_step(self, batch, batch_idx)
  
              # if you have multiple test dataloaders:
              def test_step(self, batch, batch_idx, dataloader_idxdx)
  
  
          **OPTIONAL**
          If you don't need to test you don't need to implement this method. In this step you'd normally
           generate examples or calculate anything of interest such as accuracy.
  
          When the validation_step is called, the model has been put in eval mode and PyTorch gradients
           have been disabled. At the end of validation, model goes back to training mode and gradients are enabled.
  
          The dict you return here will be available in the `test_end` method.
  
          This function is used when you execute `trainer.test()`.
  
          Example
          -------
  
          .. code-block:: python
  
              # CASE 1: A single test dataset
              def test_step(self, batch, batch_idx):
                  x, y = batch
  
                  # implement your own
                  out = self.forward(x)
                  loss = self.loss(out, y)
  
                  # calculate acc
                  labels_hat = torch.argmax(out, dim=1)
                  test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
  
                  # all optional...
                  # return whatever you need for the collation function test_end
                  output = OrderedDict({
                      'test_loss': loss_test,
                      'test_acc': torch.tensor(test_acc), # everything must be a tensor
                  })
  
                  # return an optional dict
                  return output
  
  
          If you pass in multiple test datasets, `test_step` will have an additional argument.
  
          .. code-block:: python
  
              # CASE 2: multiple test datasets
              def test_step(self, batch, batch_idx, dataset_idx):
                  # dataset_idx tells you which dataset this is.
  
  
          The `dataset_idx` corresponds to the order of datasets returned in `test_dataloader`.
          """
          pass
  
      def validation_end(self, outputs):
          """Outputs has the appended output after each validation step.
  
          :param outputs: List of outputs you defined in validation_step, or if there are multiple dataloaders,
           a list containing a list of outputs for each dataloader
          :return dict: Dictionary or OrderedDict with optional:
              progress_bar -> Dict for progress bar display. Must have only tensors
              log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)
  
          If you didn't define a validation_step, this won't be called.
           Called at the end of the validation loop with the outputs of validation_step.
  
          The outputs here are strictly for the progress bar.
           If you don't need to display anything, don't return anything.
           Any keys present in 'log', 'progress_bar' or the rest of the dictionary
           are available for callbacks to access.
  
          Example
          -------
  
          With a single dataloader
  
          .. code-block:: python
  
              def validation_end(self, outputs):
                  val_loss_mean = 0
                  val_acc_mean = 0
                  for output in outputs:
                      val_loss_mean += output['val_loss']
                      val_acc_mean += output['val_acc']
  
                  val_loss_mean /= len(outputs)
                  val_acc_mean /= len(outputs)
                  tqdm_dict = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
  
                  # show val_loss and val_acc in progress bar but only log val_loss
                  results = {
                      'progress_bar': tqdm_dict,
                      'log': {'val_loss': val_loss_mean.item()}
                  }
                  return results
  
          With multiple dataloaders, `outputs` will be a list of lists. The outer list contains
          one entry per dataloader, while the inner list contains the individual outputs of
          each validation step for that dataloader.
  
          .. code-block:: python
  
              def validation_end(self, outputs):
                  val_loss_mean = 0
                  val_acc_mean = 0
                  i = 0
                  for dataloader_outputs in outputs:
                      for output in dataloader_outputs:
                          val_loss_mean += output['val_loss']
                          val_acc_mean += output['val_acc']
                          i += 1
  
                  val_loss_mean /= i
                  val_acc_mean /= i
                  tqdm_dict = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
  
                  # show val_loss and val_acc in progress bar but only log val_loss
                  results = {
                      'progress_bar': tqdm_dict,
                      'log': {'val_loss': val_loss_mean.item()}
                  }
                  return results
  
          """
          pass
  
      def test_end(self, outputs):
          """Outputs has the appended output after each test step.
  
          :param outputs:  List of outputs you defined in test_step, or if there are multiple dataloaders,
           a list containing a list of outputs for each dataloader
          :return dict: Dict of OrderedDict with metrics to display in progress bar
  
          If you didn't define a test_step, this won't be called.
           Called at the end of the test step with the output of each test_step.
           The outputs here are strictly for the progress bar.
           If you don't need to display anything, don't return anything.
  
          Example
          -------
  
          .. code-block:: python
  
              def test_end(self, outputs):
                  test_loss_mean = 0
                  test_acc_mean = 0
                  for output in outputs:
                      test_loss_mean += output['test_loss']
                      test_acc_mean += output['test_acc']
  
                  test_loss_mean /= len(outputs)
                  test_acc_mean /= len(outputs)
                  tqdm_dict = {'test_loss': test_loss_mean.item(), 'test_acc': test_acc_mean.item()}
  
                  # show test_loss and test_acc in progress bar but only log test_loss
                  results = {
                      'progress_bar': tqdm_dict,
                      'log': {'test_loss': val_loss_mean.item()}
                  }
                  return results
  
          With multiple dataloaders, `outputs` will be a list of lists. The outer list contains
          one entry per dataloader, while the inner list contains the individual outputs of
          each validation step for that dataloader.
  
          .. code-block:: python
  
              def test_end(self, outputs):
                  test_loss_mean = 0
                  test_acc_mean = 0
                  i = 0
                  for dataloader_outputs in outputs:
                      for output in dataloader_outputs:
                          test_loss_mean += output['test_loss']
                          test_acc_mean += output['test_acc']
                          i += 1
  
                  test_loss_mean /= i
                  test_acc_mean /= i
                  tqdm_dict = {'test_loss': test_loss_mean.item(), 'test_acc': test_acc_mean.item()}
  
                  # show test_loss and test_acc in progress bar but only log test_loss
                  results = {
                      'progress_bar': tqdm_dict,
                      'log': {'test_loss': val_loss_mean.item()}
                  }
                  return results
  
          """
          pass
  
      def configure_ddp(self, model, device_ids):
          """Override to init DDP in a different way or use your own wrapper.
  
          :param model:
          :param device_ids:
          :return: DDP wrapped model
  
          Overwrite to define your own DDP implementation init.
          The only requirement is that:
          1. On a validation batch the call goes to model.validation_step.
          2. On a training batch the call goes to model.training_step.
          3. On a testing batch, the call goes to model.test_step
  
          .. code-block:: python
  
              def configure_ddp(self, model, device_ids):
                  # Lightning DDP simply routes to test_step, val_step, etc...
                  model = LightningDistributedDataParallel(
                      model,
                      device_ids=device_ids,
                      find_unused_parameters=True
                  )
                  return model
  
  
          """
          model = LightningDistributedDataParallel(
              model,
              device_ids=device_ids,
              find_unused_parameters=True
          )
          return model
  
      def init_ddp_connection(self, proc_rank, world_size):
          """Connect all procs in the world using the env:// init
          Use the first node as the root address
  
          Override to init DDP in your own way.
  
          .. code-block:: python
  
              def init_ddp_connection(self):
                  # use slurm job id for the port number
                  # guarantees unique ports across jobs from same grid search
                  try:
                      # use the last 4 numbers in the job id as the id
                      default_port = os.environ['SLURM_JOB_ID']
                      default_port = default_port[-4:]
  
                      # all ports should be in the 10k+ range
                      default_port = int(default_port) + 15000
  
                  except Exception as e:
                      default_port = 12910
  
                  # if user gave a port number, use that one instead
                  try:
                      default_port = os.environ['MASTER_PORT']
                  except Exception:
                      os.environ['MASTER_PORT'] = str(default_port)
  
                  # figure out the root node addr
                  try:
                      root_node = os.environ['SLURM_NODELIST'].split(' ')[0]
                  except Exception:
                      root_node = '127.0.0.2'
  
                  root_node = self.trainer.resolve_root_node_address(root_node)
                  os.environ['MASTER_ADDR'] = root_node
                  dist.init_process_group('nccl', rank=self.proc_rank, world_size=self.world_size)
  
          """
          # use slurm job id for the port number
          # guarantees unique ports across jobs from same grid search
          try:
              # use the last 4 numbers in the job id as the id
              default_port = os.environ['SLURM_JOB_ID']
              default_port = default_port[-4:]
  
              # all ports should be in the 10k+ range
              default_port = int(default_port) + 15000
  
          except Exception as e:
              default_port = 12910
  
          # if user gave a port number, use that one instead
          try:
              default_port = os.environ['MASTER_PORT']
          except Exception:
              os.environ['MASTER_PORT'] = str(default_port)
  
          # figure out the root node addr
          try:
              root_node = os.environ['SLURM_NODELIST'].split(' ')[0]
          except Exception:
              root_node = '127.0.0.2'
  
          root_node = self.trainer.resolve_root_node_address(root_node)
          os.environ['MASTER_ADDR'] = root_node
          dist.init_process_group('nccl', rank=proc_rank, world_size=world_size)
  
      def configure_apex(self, amp, model, optimizers, amp_level):
          """
          Override to init AMP your own way
          Must return a model and list of optimizers
          :param amp:
          :param model:
          :param optimizers:
          :param amp_level:
          :return: Apex wrapped model and optimizers
  
          Overwrite to define your own Apex implementation init.
  
          .. code-block:: python
  
              def configure_apex(self, amp, model, optimizers, amp_level):
                  model, optimizers = amp.initialize(
                      model, optimizers, opt_level=amp_level,
                  )
  
                  return model, optimizers
          """
          model, optimizers = amp.initialize(
              model, optimizers, opt_level=amp_level,
          )
  
          return model, optimizers
  
      def configure_optimizers(self):
          """Return a list of optimizers and a list of schedulers (could be empty)
  
          :return: any of these 3 options:
              - Single optimizer
              - List or Tuple - List of optimizers
              - Two lists - The first list has multiple optimizers, the second a list of learning-rate schedulers
  
          Set up as many optimizers and (optionally) learning rate schedulers as you need.
           Normally you'd need one. But in the case of GANs or something more esoteric you might have multiple.
           Lightning will call .backward() and .step() on each one in every epoch.
           If you use 16 bit precision it will also handle that.
  
          .. note:: If you use multiple optimizers, training_step will have an additional `optimizer_idx` parameter.
  
          .. note:: If you use LBFGS lightning handles the closure function automatically for you.
  
          Example
          -------
  
          .. code-block:: python
  
              # most cases
              def configure_optimizers(self):
                  opt = Adam(self.parameters(), lr=0.01)
                  return opt
  
              # multiple optimizer case (eg: GAN)
              def configure_optimizers(self):
                  generator_opt = Adam(self.model_gen.parameters(), lr=0.01)
                  disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)
                  return generator_opt, disriminator_opt
  
              # example with learning_rate schedulers
              def configure_optimizers(self):
                  generator_opt = Adam(self.model_gen.parameters(), lr=0.01)
                  disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)
                  discriminator_sched = CosineAnnealing(discriminator_opt, T_max=10)
                  return [generator_opt, disriminator_opt], [discriminator_sched]
  
          If you need to control how often those optimizers step or override the default .step() schedule,
           override the `optimizer_step` hook.
  
          """
          raise NotImplementedError
  
      def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None):
          """Do something instead of the standard optimizer behavior
  
          :param int epoch:
          :param int batch_idx:
          :param optimizer:
          :param optimizer_idx:
          :param second_order_closure: closure for second order methods
          :return:
  
          Calls `.step()` and `.zero_grad` for each optimizer.
          You can override this method to adjust how you do the optimizer step for each optimizer
  
          Called once per optimizer
  
          .. code-block:: python
  
              # DEFAULT
              def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None):
                  optimizer.step()
                  optimizer.zero_grad()
  
              # Alternating schedule for optimizer steps (ie: GANs)
              def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None):
                  # update generator opt every 2 steps
                  if optimizer_idx == 0:
                      if batch_idx % 2 == 0 :
                          optimizer.step()
                          optimizer.zero_grad()
  
                  # update discriminator opt every 4 steps
                  if optimizer_idx == 1:
                      if batch_idx % 4 == 0 :
                          optimizer.step()
                          optimizer.zero_grad()
  
                  # ...
                  # add as many optimizers as you want
  
  
          This step allows you to do a lot of non-standard training tricks such as learning-rate warm-up:
  
          .. code-block:: python
  
              # learning rate warm-up
              def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None):
                  # warm up lr
                  if self.trainer.global_step < 500:
                      lr_scale = min(1., float(self.trainer.global_step + 1) / 500.)
                      for pg in optimizer.param_groups:
                          pg['lr'] = lr_scale * self.hparams.learning_rate
  
                  # update params
                  optimizer.step()
                  optimizer.zero_grad()
  
          """
          if isinstance(optimizer, torch.optim.LBFGS):
              optimizer.step(second_order_closure)
          else:
              optimizer.step()
  
          # clear gradients
          optimizer.zero_grad()
  
      def tbptt_split_batch(self, batch, split_size):
          """
          Return list of batch splits. Each split will be passed to forward_step to enable truncated
          back propagation through time. The default implementation splits root level Tensors and
          Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.
  
          :param batch:
          :param split_size:
          :return:
  
          Called in the training loop after on_batch_start if `truncated_bptt_steps > 0`.
           Each returned batch split is passed separately to training_step(...).
  
          .. code-block:: python
  
              def tbptt_split_batch(self, batch, split_size):
                splits = []
                for t in range(0, time_dims[0], split_size):
                    batch_split = []
                    for i, x in enumerate(batch):
                        if isinstance(x, torch.Tensor):
                            split_x = x[:, t:t + split_size]
                        elif isinstance(x, collections.Sequence):
                            split_x = [None] * len(x)
                            for batch_idx in range(len(x)):
                                split_x[batch_idx] = x[batch_idx][t:t + split_size]
  
                        batch_split.append(split_x)
  
                    splits.append(batch_split)
  
                return splits
          """
          time_dims = [len(x[0]) for x in batch if isinstance(
              x, torch.Tensor) or isinstance(x, collections.Sequence)]
          assert len(time_dims) >= 1, "Unable to determine batch time dimension"
          assert all(x == time_dims[0] for x in time_dims), "Batch time dimension length is ambiguous"
  
          splits = []
          for t in range(0, time_dims[0], split_size):
              batch_split = []
              for i, x in enumerate(batch):
                  if isinstance(x, torch.Tensor):
                      split_x = x[:, t:t + split_size]
                  elif isinstance(x, collections.Sequence):
                      split_x = [None] * len(x)
                      for batch_idx in range(len(x)):
                          split_x[batch_idx] = x[batch_idx][t:t + split_size]
  
                  batch_split.append(split_x)
  
              splits.append(batch_split)
  
          return splits
  
      @data_loader
      def train_dataloader(self):
          """Implement a PyTorch DataLoader
  
          :return: PyTorch DataLoader
  
          Called by lightning during training loop. Make sure to use the @pl.data_loader decorator,
           this ensures not calling this function until the data are needed.
           If you want to change the data during every epoch DON'T use the data_loader decorator.
  
          Example
          -------
  
          .. code-block:: python
  
              @pl.data_loader
              def train_dataloader(self):
                  transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
                  dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform, download=True)
                  loader = torch.utils.data.DataLoader(
                      dataset=dataset,
                      batch_size=self.hparams.batch_size,
                      shuffle=True
                  )
                  return loader
  
  
          """
          raise NotImplementedError
  
      @data_loader
      def tng_dataloader(self):
          """Implement a PyTorch DataLoader.
  
          .. warning:: Deprecated in v0.5.0. use train_dataloader instead.
          """
          try:
              output = self.tng_dataloader()
              warnings.warn("`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0"
                            " and will be removed in v0.8.0", DeprecationWarning)
              return output
          except NotImplementedError:
              raise NotImplementedError
  
      @data_loader
      def test_dataloader(self):
          """Implement a PyTorch DataLoader.
  
          :return: PyTorch DataLoader
  
          If you don't need a test dataset and a test_step, you don't need to implement this method.
  
          Called by lightning during test loop. Make sure to use the @pl.data_loader decorator,
           this ensures not calling this function until the data are needed.
           If you want to change the data during every epoch DON'T use the data_loader decorator.
  
          Example
          -------
  
          .. code-block:: python
  
              @pl.data_loader
              def test_dataloader(self):
                  transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
                  dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform, download=True)
                  loader = torch.utils.data.DataLoader(
                      dataset=dataset,
                      batch_size=self.hparams.batch_size,
                      shuffle=True
                  )
  
                  return loader
  
          """
          return None
  
      @data_loader
      def val_dataloader(self):
          """Implement a PyTorch DataLoader.
  
          :return: PyTorch DataLoader or list of PyTorch Dataloaders.
  
          If you don't need a validation dataset and a validation_step, you don't need to implement this method.
  
          Called by lightning during validation loop. Make sure to use the @pl.data_loader decorator,
           this ensures not calling this function until the data are needed.
           If you want to change the data during every epoch DON'T use the data_loader decorator.
  
          Example
          -------
  
          .. code-block:: python
  
              @pl.data_loader
              def val_dataloader(self):
                  transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
                  dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform, download=True)
                  loader = torch.utils.data.DataLoader(
                      dataset=dataset,
                      batch_size=self.hparams.batch_size,
                      shuffle=True
                  )
  
                  return loader
  
              # can also return multiple dataloaders
              @pl.data_loader
              def val_dataloader(self):
                  return [loader_a, loader_b, ..., loader_n]
  
          In the case where you return multiple `val_dataloaders`, the `validation_step`
           will have an arguement `dataset_idx` which matches the order here.
          """
          return None
  
      @classmethod
      def load_from_metrics(cls, weights_path, tags_csv, map_location=None):
          """Primary way of loading model from csv weights path.
  
          :param str weights_path: Path to a PyTorch checkpoint
          :param str tags_csv: Path to meta_tags.csv file generated by the test-tube Experiment
          :param dict map_location: A dictionary mapping saved weight GPU devices to new GPU devices
              for mapping storage {'cuda:1':'cuda:0'}
          :return: The pretrained LightningModule
  
          If you're using test tube, there is an alternate method which uses the meta_tags.csv
          file from test-tube to rebuild the model. The meta_tags.csv file can be found in the
          test-tube experiment save_dir.
  
          .. code-block:: python
  
              pretrained_model = MyLightningModule.load_from_metrics(
                  weights_path='/path/to/pytorch_checkpoint.ckpt',
                  tags_csv='/path/to/test_tube/experiment/version/meta_tags.csv',
                  on_gpu=True,
                  map_location=None
              )
  
              # predict
              pretrained_model.eval()
              pretrained_model.freeze()
              y_hat = pretrained_model(x)
  
          This is the easiest/fastest way which loads hyperparameters and weights from a checkpoint,
          such as the one saved by the `ModelCheckpoint` callback
  
          .. code-block:: python
  
              pretrained_model = MyLightningModule.load_from_checkpoint(
                  checkpoint_path='/path/to/pytorch_checkpoint.ckpt'
              )
  
              # predict
              pretrained_model.eval()
              pretrained_model.freeze()
              y_hat = pretrained_model(x)
  
          """
          hparams = load_hparams_from_tags_csv(tags_csv)
          hparams.__setattr__('on_gpu', False)
  
          if map_location is not None:
              checkpoint = torch.load(weights_path, map_location=map_location)
          else:
              checkpoint = torch.load(weights_path, map_location=lambda storage, loc: storage)
  
          # load the state_dict on the model automatically
          model = cls(hparams)
          model.load_state_dict(checkpoint['state_dict'])
  
          # give model a chance to load something
          model.on_load_checkpoint(checkpoint)
  
          return model
  
      @classmethod
      def load_from_checkpoint(cls, checkpoint_path, map_location=None):
          """
          Primary way of loading model from a checkpoint
          :param checkpoint_path:
          :param map_location: dic for mapping storage {'cuda:1':'cuda:0'}
          :return:
          """
  
          if map_location is not None:
              checkpoint = torch.load(checkpoint_path, map_location=map_location)
          else:
              checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
  
          try:
              ckpt_hparams = checkpoint['hparams']
          except KeyError:
              raise IOError(
                  "Checkpoint does not contain hyperparameters. Are your model hyperparameters stored"
                  "in self.hparams?"
              )
          hparams = Namespace(**ckpt_hparams)
  
          # load the state_dict on the model automatically
          model = cls(hparams)
          model.load_state_dict(checkpoint['state_dict'])
  
          # give model a chance to load something
          model.on_load_checkpoint(checkpoint)
  
          return model
  
      def summarize(self, mode):
          model_summary = ModelSummary(self, mode=mode)
          logging.info('\n' + model_summary.__str__())
  
      def freeze(self):
          """Freeze all params for inference
  
          .. code-block:: python
  
              model = MyLightningModule(...)
              model.freeze()
  
          """
          for param in self.parameters():
              param.requires_grad = False
  
          self.eval()
  
      def unfreeze(self):
          """Unfreeze all params for inference.
  
          .. code-block:: python
  
              model = MyLightningModule(...)
              model.unfreeze()
  
          """
          for param in self.parameters():
              param.requires_grad = True
  
          self.train()
  
      def on_load_checkpoint(self, checkpoint):
          """
  
          :param checkpoint:
  
          Called by lightning to restore your model. Lighting auto-restores global step, epoch, etc...
           It also restores the model state_dict.
           If you saved something with **on_save_checkpoint** this is your chance to restore this.
  
          Example
          -------
  
          .. code-block:: python
  
              def on_load_checkpoint(self, checkpoint):
                  # 99% of the time you don't need to implement this method
                  self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']
  
          """
          pass
  
      def on_save_checkpoint(self, checkpoint):
          """
  
          :param checkpoint:
  
          Called by lightning to checkpoint your model. Lightning saves the training state
           (current epoch, global_step, etc) and also saves the model state_dict.
           If you want to save anything else, use this method to add your own key-value pair.
  
          Example
          -------
  
          .. code-block:: python
  
              def on_save_checkpoint(self, checkpoint):
                  # 99% of use cases you don't need to implement this method
                  checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object
  
          """
          pass
  