text: # Copyright The PyTorch Lightning team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """nn.Module with additional great features."""
  
  import collections
  import copy
  import inspect
  import os
  import re
  import tempfile
  import uuid
  from abc import ABC
  from argparse import Namespace
  from functools import partial
  from pathlib import Path
  from typing import Any, Callable, Dict, List, Optional, Tuple, TYPE_CHECKING, Union
  
  import torch
  from torch import ScriptModule, Tensor
  from torch.nn import Module
  from torch.optim.optimizer import Optimizer
  
  from pytorch_lightning import _logger as log
  from pytorch_lightning.core.grads import GradInformation
  from pytorch_lightning.core.hooks import CheckpointHooks, DataHooks, ModelHooks
  from pytorch_lightning.core.memory import ModelSummary
  from pytorch_lightning.core.optimizer import LightningOptimizer
  from pytorch_lightning.core.saving import ALLOWED_CONFIG_TYPES, ModelIO, PRIMITIVE_TYPES
  from pytorch_lightning.core.step_result import Result
  from pytorch_lightning.utilities import rank_zero_warn
  from pytorch_lightning.utilities.apply_func import apply_to_collection, convert_to_tensors
  from pytorch_lightning.utilities.device_dtype_mixin import DeviceDtypeModuleMixin
  from pytorch_lightning.utilities.exceptions import MisconfigurationException
  from pytorch_lightning.utilities.parsing import AttributeDict, collect_init_args, get_init_args
  
  if TYPE_CHECKING:
      from pytorch_lightning.trainer.states import RunningStage
  
  
  class LightningModule(
      ABC,
      DeviceDtypeModuleMixin,
      GradInformation,
      ModelIO,
      ModelHooks,
      DataHooks,
      CheckpointHooks,
      Module,
  ):
      # Below is for property support of JIT in PyTorch 1.7
      # since none of them is important when using JIT, we are going to ignore them.
      __jit_unused_properties__ = [
          "datamodule",
          "example_input_array",
          "hparams",
          "hparams_initial",
          "on_gpu",
          "current_epoch",
          "global_step",
          "running_stage",
          "global_rank",
          "local_rank",
          "logger",
          "model_size",
      ] + DeviceDtypeModuleMixin.__jit_unused_properties__
  
      def __init__(self, *args, **kwargs):
          super().__init__(*args, **kwargs)
  
          # see (https://github.com/pytorch/pytorch/blob/3e6bb5233f9ca2c5aa55d9cda22a7ee85439aa6e/
          # torch/nn/modules/module.py#L227)
          torch._C._log_api_usage_once(f"lightning.module.{self.__class__.__name__}")
  
          self.exp_save_path = None
  
          self.loaded_optimizer_states_dict = {}
  
          #: Pointer to the trainer object
          self.trainer = None
  
          self._distrib_type = None
          self._device_type = None
  
          #: True if using amp
          self.use_amp = False
  
          #: The precision used
          self.precision = 32
  
          # optionally can be set by user
          self._example_input_array = None
          self._datamodule = None
          self._results: Optional[Result] = None
          self._current_fx_name = ''
          self._running_manual_backward = False
          self._current_hook_fx_name = None
          self._current_dataloader_idx = None
          self._automatic_optimization: bool = True
  
      def optimizers(self, use_pl_optimizer: bool = True) -> Union[Optimizer, List[Optimizer], List[LightningOptimizer]]:
          if use_pl_optimizer:
              opts = list(self.trainer.lightning_optimizers.values())
          else:
              opts = self.trainer.optimizers
  
          # single optimizer
          if isinstance(opts, list) and len(opts) == 1 and isinstance(opts[0], Optimizer):
              return opts[0]
          # multiple opts
          return opts
  
      @property
      def example_input_array(self) -> Any:
          return self._example_input_array
  
      @property
      def current_epoch(self) -> int:
          """The current epoch"""
          return self.trainer.current_epoch if self.trainer else 0
  
      @property
      def global_step(self) -> int:
          """Total training batches seen across all epochs"""
          return self.trainer.global_step if self.trainer else 0
  
      @property
      def global_rank(self) -> int:
          """ The index of the current process across all nodes and devices. """
          return self.trainer.global_rank if self.trainer else 0
  
      @property
      def local_rank(self) -> int:
          """ The index of the current process within a single node. """
          return self.trainer.local_rank if self.trainer else 0
  
      @example_input_array.setter
      def example_input_array(self, example: Any) -> None:
          self._example_input_array = example
  
      @property
      def datamodule(self) -> Any:
          return self._datamodule
  
      @datamodule.setter
      def datamodule(self, datamodule: Any) -> None:
          self._datamodule = datamodule
  
      @property
      def on_gpu(self):
          """
          True if your model is currently running on GPUs.
          Useful to set flags around the LightningModule for different CPU vs GPU behavior.
          """
          return self.device.type == "cuda"
  
      @property
      def automatic_optimization(self) -> bool:
          """
          If False you are responsible for calling .backward, .step, zero_grad.
          """
          return self._automatic_optimization
  
      @property
      def running_stage(self) -> Optional["RunningStage"]:
          return self.trainer._running_stage if self.trainer else None
  
      @automatic_optimization.setter
      def automatic_optimization(self, automatic_optimization: bool) -> None:
          self._automatic_optimization = automatic_optimization
  
      @property
      def logger(self):
          """ Reference to the logger object in the Trainer. """
          return self.trainer.logger if self.trainer else None
  
      def _apply_batch_transfer_handler(self, batch: Any, device: Optional[torch.device] = None, dataloader_idx: int = 0):
          batch = self.on_before_batch_transfer(batch, dataloader_idx)
          batch = self.transfer_batch_to_device(batch, device)
          batch = self.on_after_batch_transfer(batch, dataloader_idx)
          return batch
  
      def print(self, *args, **kwargs) -> None:
          r"""
          Prints only from process 0. Use this in any distributed mode to log only once.
  
          Args:
              *args: The thing to print. The same as for Python's built-in print function.
              **kwargs: The same as for Python's built-in print function.
  
          Example::
  
              def forward(self, x):
                  self.print(x, 'in forward')
  
          """
          if self.trainer.is_global_zero:
              progress_bar = self.trainer.progress_bar_callback
              if progress_bar is not None and progress_bar.is_enabled:
                  progress_bar.print(*args, **kwargs)
              else:
                  print(*args, **kwargs)
  
      def log(
          self,
          name: str,
          value: Any,
          prog_bar: bool = False,
          logger: bool = True,
          on_step: Optional[bool] = None,
          on_epoch: Optional[bool] = None,
          reduce_fx: Callable = torch.mean,
          tbptt_reduce_fx: Callable = torch.mean,
          tbptt_pad_token: int = 0,
          enable_graph: bool = False,
          sync_dist: bool = False,
          sync_dist_op: Union[Any, str] = 'mean',
          sync_dist_group: Optional[Any] = None,
      ):
          """
          Log a key, value
  
          Example::
  
              self.log('train_loss', loss)
  
          The default behavior per hook is as follows
  
          .. csv-table:: ``*`` also applies to the test loop
             :header: "LightningMoule Hook", "on_step", "on_epoch", "prog_bar", "logger"
             :widths: 20, 10, 10, 10, 10
  
             "training_step", "T", "F", "F", "T"
             "training_step_end", "T", "F", "F", "T"
             "training_epoch_end", "F", "T", "F", "T"
             "validation_step*", "F", "T", "F", "T"
             "validation_step_end*", "F", "T", "F", "T"
             "validation_epoch_end*", "F", "T", "F", "T"
  
          Args:
              name: key name
              value: value name
              prog_bar: if True logs to the progress bar
              logger: if True logs to the logger
              on_step: if True logs at this step. None auto-logs at the training_step but not validation/test_step
              on_epoch: if True logs epoch accumulated metrics. None auto-logs at the val/test step but not training_step
              reduce_fx: reduction function over step values for end of epoch. Torch.mean by default
              tbptt_reduce_fx: function to reduce on truncated back prop
              tbptt_pad_token: token to use for padding
              enable_graph: if True, will not auto detach the graph
              sync_dist: if True, reduces the metric across GPUs/TPUs
              sync_dist_op: the op to sync across GPUs/TPUs
              sync_dist_group: the ddp group
          """
          if self._results is not None:
              # in any epoch end can't log step metrics (only epoch metric)
              if 'epoch_end' in self._current_fx_name and on_step:
                  m = f'on_step=True cannot be used on {self._current_fx_name} method'
                  raise MisconfigurationException(m)
  
              if 'epoch_end' in self._current_fx_name and on_epoch is False:
                  m = f'on_epoch cannot be False when called from the {self._current_fx_name} method'
                  raise MisconfigurationException(m)
  
              # add log_dict
              # TODO: if logged twice fail with crash
  
              # set the default depending on the fx_name
              on_step = self.__auto_choose_log_on_step(on_step)
              on_epoch = self.__auto_choose_log_on_epoch(on_epoch)
  
              if self._current_hook_fx_name is not None:
                  self.trainer.logger_connector.check_logging_in_callbacks(
                      self._current_hook_fx_name, on_step=on_step, on_epoch=on_epoch
                  )
  
              # make sure user doesn't introduce logic for multi-dataloaders
              if "/dataloader_idx_" in name:
                  raise MisconfigurationException(
                      f"Logged key: {name} should not contain information about dataloader_idx."
                  )
  
              training_type_plugin = self.trainer.training_type_plugin
  
              self._results.log(
                  name,
                  value,
                  prog_bar,
                  logger,
                  on_step,
                  on_epoch,
                  reduce_fx,
                  tbptt_reduce_fx,
                  tbptt_pad_token,
                  enable_graph,
                  sync_dist,
                  sync_dist_op,
                  sync_dist_group,
                  training_type_plugin.reduce,
                  self._current_dataloader_idx,
                  self.device,
              )
  
      def log_dict(
          self,
          dictionary: dict,
          prog_bar: bool = False,
          logger: bool = True,
          on_step: Optional[bool] = None,
          on_epoch: Optional[bool] = None,
          reduce_fx: Callable = torch.mean,
          tbptt_reduce_fx: Callable = torch.mean,
          tbptt_pad_token: int = 0,
          enable_graph: bool = False,
          sync_dist: bool = False,
          sync_dist_op: Union[Any, str] = 'mean',
          sync_dist_group: Optional[Any] = None,
      ):
          """
          Log a dictonary of values at once
  
          Example::
  
              values = {'loss': loss, 'acc': acc, ..., 'metric_n': metric_n}
              self.log_dict(values)
  
          Args:
              dictionary: key value pairs (str, tensors)
              prog_bar: if True logs to the progress base
              logger: if True logs to the logger
              on_step: if True logs at this step. None auto-logs for training_step but not validation/test_step
              on_epoch: if True logs epoch accumulated metrics. None auto-logs for val/test step but not training_step
              reduce_fx: reduction function over step values for end of epoch. Torch.mean by default
              tbptt_reduce_fx: function to reduce on truncated back prop
              tbptt_pad_token: token to use for padding
              enable_graph: if True, will not auto detach the graph
              sync_dist: if True, reduces the metric across GPUs/TPUs
              sync_dist_op: the op to sync across GPUs/TPUs
              sync_dist_group: the ddp group:
          """
          for k, v in dictionary.items():
              self.log(
                  name=k,
                  value=v,
                  prog_bar=prog_bar,
                  logger=logger,
                  on_step=on_step,
                  on_epoch=on_epoch,
                  reduce_fx=reduce_fx,
                  enable_graph=enable_graph,
                  sync_dist=sync_dist,
                  sync_dist_group=sync_dist_group,
                  sync_dist_op=sync_dist_op,
                  tbptt_pad_token=tbptt_pad_token,
                  tbptt_reduce_fx=tbptt_reduce_fx,
              )
  
      def write_prediction(
          self, name: str, value: Union[torch.Tensor, List[torch.Tensor]], filename: str = 'predictions.pt'
      ):
          """
          Write predictions to disk using ``torch.save``
  
          Example::
  
              self.write_prediction('pred', torch.tensor(...), filename='my_predictions.pt')
  
          Args:
              name: a string indicating the name to save the predictions under
              value: the predictions, either a single :class:`~torch.Tensor` or a list of them
              filename: name of the file to save the predictions to
  
          Note:
              when running in distributed mode, calling ``write_prediction`` will create a file for
              each device with respective names: ``filename_rank_0.pt``, ``filename_rank_1.pt``, ...
  
          """
          self.trainer.evaluation_loop.predictions._add_prediction(name, value, filename)
  
      def write_prediction_dict(self, predictions_dict: Dict[str, Any], filename: str = 'predictions.pt'):
          """
          Write a dictonary of predictions to disk at once using ``torch.save``
  
          Example::
  
              pred_dict = {'pred1': torch.tensor(...), 'pred2': torch.tensor(...)}
              self.write_prediction_dict(pred_dict)
  
          Args:
              predictions_dict: dict containing predictions, where each prediction should
                  either be single :class:`~torch.Tensor` or a list of them
  
          Note:
              when running in distributed mode, calling ``write_prediction_dict`` will create a file for
              each device with respective names: ``filename_rank_0.pt``, ``filename_rank_1.pt``, ...
  
          """
          for k, v in predictions_dict.items():
              self.write_prediction(k, v, filename)
  
      def __auto_choose_log_on_step(self, on_step):
          if on_step is None:
              if self._current_fx_name in {'training_step', 'training_step_end'}:
                  on_step = True
              elif self._current_fx_name in {
                  'evaluation_step', 'evaluation_step_end', 'evaluation_epoch_end', 'training_epoch_end'
              }:
                  on_step = False
              else:
                  on_step = False
  
          return on_step
  
      def __auto_choose_log_on_epoch(self, on_epoch):
          if on_epoch is None:
              if self._current_fx_name in {'training_step', 'training_step_end'}:
                  on_epoch = False
              elif self._current_fx_name in {
                  'evaluation_step', 'evaluation_step_end', 'evaluation_epoch_end', 'training_epoch_end'
              }:
                  on_epoch = True
              else:
                  on_epoch = True
  
          return on_epoch
  
      def all_gather(
          self,
          data: Union[torch.Tensor, Dict, List, Tuple],
          group: Optional[Any] = None,
          sync_grads: bool = False,
      ):
          r"""
          Allows users to call ``self.all_gather()`` from the LightningModule, thus making
          the ```all_gather``` operation accelerator agnostic.
  
          ```all_gather``` is a function provided by accelerators to gather a tensor from several
          distributed processes
  
          Args:
              tensor: int, float, tensor of shape (batch, ...), or a (possibly nested) collection thereof.
              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, ...), or if the input was a collection
              the output will also be a collection with tensors of this shape.
          """
          group = group if group is not None else torch.distributed.group.WORLD
          all_gather = self.trainer.accelerator.all_gather
          data = convert_to_tensors(data, device=self.device)
          all_gather = partial(all_gather, group=group, sync_grads=sync_grads)
          return apply_to_collection(data, torch.Tensor, all_gather)
  
      def forward(self, *args, **kwargs):
          r"""
          Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to define
          the operations you want to use for prediction (i.e.: on a server or as a feature extractor).
  
          Normally you'd call ``self()`` from your :meth:`training_step` method.
          This makes it easy to write a complex system for training with the outputs
          you'd want in a prediction setting.
  
          You may also find the :func:`~pytorch_lightning.core.decorators.auto_move_data` decorator useful
          when using the module outside Lightning in a production setting.
  
          Args:
              *args: Whatever you decide to pass into the forward method.
              **kwargs: Keyword arguments are also possible.
  
          Return:
              Predicted output
  
          Examples::
  
              # example if we were using this model as a feature extractor
              def forward(self, x):
                  feature_maps = self.convnet(x)
                  return feature_maps
  
              def training_step(self, batch, batch_idx):
                  x, y = batch
                  feature_maps = self(x)
                  logits = self.classifier(feature_maps)
  
                  # ...
                  return loss
  
              # splitting it this way allows model to be used a feature extractor
              model = MyModelAbove()
  
              inputs = server.get_request()
              results = model(inputs)
              server.write_results(results)
  
              # -------------
              # This is in stark contrast to torch.nn.Module where normally you would have this:
              def forward(self, batch):
                  x, y = batch
                  feature_maps = self.convnet(x)
                  logits = self.classifier(feature_maps)
                  return logits
  
          """
          return super().forward(*args, **kwargs)
  
      def training_step(self, *args, **kwargs):
          r"""
          Here you compute and return the training loss and some additional metrics for e.g.
          the progress bar or logger.
  
          Args:
              batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
                  The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
              batch_idx (int): Integer displaying index of this batch
              optimizer_idx (int): When using multiple optimizers, this argument will also be present.
              hiddens(:class:`~torch.Tensor`): Passed in if
                  :paramref:`~pytorch_lightning.trainer.trainer.Trainer.truncated_bptt_steps` > 0.
  
          Return:
              Any of.
  
              - :class:`~torch.Tensor` - The loss tensor
              - ``dict`` - A dictionary. Can include any keys, but must include the key ``'loss'``
              - ``None`` - Training will skip to the next batch
  
          Note:
              Returning ``None`` is currently not supported for multi-GPU or TPU, or with 16-bit precision enabled.
  
          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 model specific.
  
          Example::
  
              def training_step(self, batch, batch_idx):
                  x, y, z = batch
                  out = self.encoder(x)
                  loss = self.loss(out, x)
                  return loss
  
          If you define multiple optimizers, this step will be called with an additional
          ``optimizer_idx`` parameter.
  
          .. code-block:: python
  
              # Multiple optimizers (e.g.: 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 hidden states from the previous truncated backprop step
                  ...
                  out, hiddens = self.lstm(data, hiddens)
                  ...
                  return {'loss': loss, 'hiddens': hiddens}
  
          Note:
              The loss value shown in the progress bar is smoothed (averaged) over the last values,
              so it differs from the actual loss returned in train/validation step.
          """
          rank_zero_warn("`training_step` must be implemented to be used with the Lightning Trainer")
  
      def training_step_end(self, *args, **kwargs):
          """
          Use this when training with dp or ddp2 because :meth:`training_step`
          will operate on only part of the batch. However, this is still optional
          and only needed for things like softmax or NCE loss.
  
          Note:
              If you later switch to ddp or some other mode, this will still be called
              so that you don't have to change your code
  
          .. code-block:: python
  
              # pseudocode
              sub_batches = split_batches_for_dp(batch)
              batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]
              training_step_end(batch_parts_outputs)
  
          Args:
              batch_parts_outputs: What you return in `training_step` for each batch part.
  
          Return:
              Anything
  
          When using dp/ddp2 distributed backends, only a portion of the batch is inside the training_step:
  
          .. code-block:: python
  
              def training_step(self, batch, batch_idx):
                  # batch is 1/num_gpus big
                  x, y = batch
  
                  out = self(x)
  
                  # softmax uses only a portion of the batch in the denomintaor
                  loss = self.softmax(out)
                  loss = nce_loss(loss)
                  return loss
  
          If you wish to do something with all the parts of the batch, then use this method to do it:
  
          .. code-block:: python
  
              def training_step(self, batch, batch_idx):
                  # batch is 1/num_gpus big
                  x, y = batch
  
                  out = self.encoder(x)
                  return {'pred': out}
  
              def training_step_end(self, training_step_outputs):
                  gpu_0_pred = training_step_outputs[0]['pred']
                  gpu_1_pred = training_step_outputs[1]['pred']
                  gpu_n_pred = training_step_outputs[n]['pred']
  
                  # this softmax now uses the full batch
                  loss = nce_loss([gpu_0_pred, gpu_1_pred, gpu_n_pred])
                  return loss
  
          See Also:
              See the :ref:`advanced/multi_gpu:Multi-GPU training` guide for more details.
          """
  
      def training_epoch_end(self, outputs: List[Any]) -> None:
          """
          Called at the end of the training epoch with the outputs of all training steps.
          Use this in case you need to do something with all the outputs for every training_step.
  
          .. code-block:: python
  
              # the pseudocode for these calls
              train_outs = []
              for train_batch in train_data:
                  out = training_step(train_batch)
                  train_outs.append(out)
              training_epoch_end(train_outs)
  
          Args:
              outputs: List of outputs you defined in :meth:`training_step`, or if there are
                  multiple dataloaders, a list containing a list of outputs for each dataloader.
  
          Return:
              None
  
          Note:
              If this method is not overridden, this won't be called.
  
          Example::
  
              def training_epoch_end(self, training_step_outputs):
                  # do something with all training_step outputs
                  return result
  
          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 training step for that dataloader.
  
          .. code-block:: python
  
              def training_epoch_end(self, training_step_outputs):
                  for out in training_step_outputs:
                      # do something here
          """
  
      def validation_step(self, *args, **kwargs):
          r"""
          Operates on a single batch of data from the validation set.
          In this step you'd might generate examples or calculate anything of interest like accuracy.
  
          .. code-block:: python
  
              # the pseudocode for these calls
              val_outs = []
              for val_batch in val_data:
                  out = validation_step(val_batch)
                  val_outs.append(out)
              validation_epoch_end(val_outs)
  
          Args:
              batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
                  The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
              batch_idx (int): The index of this batch
              dataloader_idx (int): The index of the dataloader that produced this batch
                  (only if multiple val dataloaders used)
  
          Return:
             Any of.
  
              - Any object or value
              - ``None`` - Validation will skip to the next batch
  
          .. code-block:: python
  
              # pseudocode of order
              out = validation_step()
              if defined('validation_step_end'):
                  out = validation_step_end(out)
              out = validation_epoch_end(out)
  
  
          .. 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_idx)
  
          Examples::
  
              # CASE 1: A single validation dataset
              def validation_step(self, batch, batch_idx):
                  x, y = batch
  
                  # implement your own
                  out = self(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)
  
                  # log the outputs!
                  self.log_dict({'val_loss': loss, 'val_acc': val_acc})
  
          If you pass in multiple val dataloaders, :meth:`validation_step` will have an additional argument.
  
          .. code-block:: python
  
              # CASE 2: multiple validation dataloaders
              def validation_step(self, batch, batch_idx, dataloader_idx):
                  # dataloader_idx tells you which dataset this is.
  
          Note:
              If you don't need to validate you don't need to implement this method.
  
          Note:
              When the :meth:`validation_step` is called, the model has been put in eval mode
              and PyTorch gradients have been disabled. At the end of validation,
              the model goes back to training mode and gradients are enabled.
          """
  
      def validation_step_end(self, *args, **kwargs):
          """
          Use this when validating with dp or ddp2 because :meth:`validation_step`
          will operate on only part of the batch. However, this is still optional
          and only needed for things like softmax or NCE loss.
  
          Note:
              If you later switch to ddp or some other mode, this will still be called
              so that you don't have to change your code.
  
          .. code-block:: python
  
              # pseudocode
              sub_batches = split_batches_for_dp(batch)
              batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]
              validation_step_end(batch_parts_outputs)
  
          Args:
              batch_parts_outputs: What you return in :meth:`validation_step`
                  for each batch part.
  
          Return:
              None or anything
  
          .. code-block:: python
  
              # WITHOUT validation_step_end
              # if used in DP or DDP2, this batch is 1/num_gpus large
              def validation_step(self, batch, batch_idx):
                  # batch is 1/num_gpus big
                  x, y = batch
  
                  out = self.encoder(x)
                  loss = self.softmax(out)
                  loss = nce_loss(loss)
                  self.log('val_loss', loss)
  
              # --------------
              # with validation_step_end to do softmax over the full batch
              def validation_step(self, batch, batch_idx):
                  # batch is 1/num_gpus big
                  x, y = batch
  
                  out = self(x)
                  return out
  
              def validation_step_end(self, val_step_outputs):
                  for out in val_step_outputs:
                      # do something with these
  
          See Also:
              See the :ref:`advanced/multi_gpu:Multi-GPU training` guide for more details.
          """
  
      def validation_epoch_end(self, outputs: List[Any]) -> None:
          """
          Called at the end of the validation epoch with the outputs of all validation steps.
  
          .. code-block:: python
  
              # the pseudocode for these calls
              val_outs = []
              for val_batch in val_data:
                  out = validation_step(val_batch)
                  val_outs.append(out)
              validation_epoch_end(val_outs)
  
          Args:
              outputs: List of outputs you defined in :meth:`validation_step`, or if there
                  are multiple dataloaders, a list containing a list of outputs for each dataloader.
  
          Return:
              None
  
          Note:
              If you didn't define a :meth:`validation_step`, this won't be called.
  
          Examples:
              With a single dataloader:
  
              .. code-block:: python
  
                  def validation_epoch_end(self, val_step_outputs):
                      for out in val_step_outputs:
                          # do something
  
              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_epoch_end(self, outputs):
                      for dataloader_output_result in outputs:
                          dataloader_outs = dataloader_output_result.dataloader_i_outputs
  
                      self.log('final_metric', final_value)
          """
  
      def test_step(self, *args, **kwargs):
          r"""
          Operates on a single batch of data from the test set.
          In this step you'd normally generate examples or calculate anything of interest
          such as accuracy.
  
          .. code-block:: python
  
              # the pseudocode for these calls
              test_outs = []
              for test_batch in test_data:
                  out = test_step(test_batch)
                  test_outs.append(out)
              test_epoch_end(test_outs)
  
          Args:
              batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
                  The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
              batch_idx (int): The index of this batch.
              dataloader_idx (int): The index of the dataloader that produced this batch
                  (only if multiple test dataloaders used).
  
          Return:
             Any of.
  
              - Any object or value
              - ``None`` - Testing will skip to the next batch
  
          .. 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_idx)
  
          Examples::
  
              # CASE 1: A single test dataset
              def test_step(self, batch, batch_idx):
                  x, y = batch
  
                  # implement your own
                  out = self(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)
                  test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
  
                  # log the outputs!
                  self.log_dict({'test_loss': loss, 'test_acc': test_acc})
  
          If you pass in multiple test dataloaders, :meth:`test_step` will have an additional argument.
  
          .. code-block:: python
  
              # CASE 2: multiple test dataloaders
              def test_step(self, batch, batch_idx, dataloader_idx):
                  # dataloader_idx tells you which dataset this is.
  
          Note:
              If you don't need to test you don't need to implement this method.
  
          Note:
              When the :meth:`test_step` is called, the model has been put in eval mode and
              PyTorch gradients have been disabled. At the end of the test epoch, the model goes back
              to training mode and gradients are enabled.
          """
  
      def test_step_end(self, *args, **kwargs):
          """
          Use this when testing with dp or ddp2 because :meth:`test_step` will operate
          on only part of the batch. However, this is still optional
          and only needed for things like softmax or NCE loss.
  
          Note:
              If you later switch to ddp or some other mode, this will still be called
              so that you don't have to change your code.
  
          .. code-block:: python
  
              # pseudocode
              sub_batches = split_batches_for_dp(batch)
              batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]
              test_step_end(batch_parts_outputs)
  
          Args:
              batch_parts_outputs: What you return in :meth:`test_step` for each batch part.
  
          Return:
              None or anything
  
          .. code-block:: python
  
              # WITHOUT test_step_end
              # if used in DP or DDP2, this batch is 1/num_gpus large
              def test_step(self, batch, batch_idx):
                  # batch is 1/num_gpus big
                  x, y = batch
  
                  out = self(x)
                  loss = self.softmax(out)
                  self.log('test_loss', loss)
  
              # --------------
              # with test_step_end to do softmax over the full batch
              def test_step(self, batch, batch_idx):
                  # batch is 1/num_gpus big
                  x, y = batch
  
                  out = self.encoder(x)
                  return out
  
              def test_step_end(self, output_results):
                  # this out is now the full size of the batch
                  all_test_step_outs = output_results.out
                  loss = nce_loss(all_test_step_outs)
                  self.log('test_loss', loss)
  
          See Also:
              See the :ref:`advanced/multi_gpu:Multi-GPU training` guide for more details.
          """
  
      def test_epoch_end(self, outputs: List[Any]) -> None:
          """
          Called at the end of a test epoch with the output of all test steps.
  
          .. code-block:: python
  
              # the pseudocode for these calls
              test_outs = []
              for test_batch in test_data:
                  out = test_step(test_batch)
                  test_outs.append(out)
              test_epoch_end(test_outs)
  
          Args:
              outputs: List of outputs you defined in :meth:`test_step_end`, or if there
                  are multiple dataloaders, a list containing a list of outputs for each dataloader
  
          Return:
              None
  
          Note:
              If you didn't define a :meth:`test_step`, this won't be called.
  
          Examples:
              With a single dataloader:
  
              .. code-block:: python
  
                  def test_epoch_end(self, outputs):
                      # do something with the outputs of all test batches
                      all_test_preds = test_step_outputs.predictions
  
                      some_result = calc_all_results(all_test_preds)
                      self.log(some_result)
  
              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 test step for that dataloader.
  
              .. code-block:: python
  
                  def test_epoch_end(self, outputs):
                      final_value = 0
                      for dataloader_outputs in outputs:
                          for test_step_out in dataloader_outputs:
                              # do something
                              final_value += test_step_out
  
                      self.log('final_metric', final_value)
          """
  
      def predict(self, batch: Any, batch_idx: int, dataloader_idx: Optional[int] = None):
          """
          Use this function with trainer.predict(...). Override if you need to add any processing logic.
          """
          return self(batch)
  
      def configure_callbacks(self):
          """
          Configure model-specific callbacks.
          When the model gets attached, e.g., when ``.fit()`` or ``.test()`` gets called,
          the list returned here will be merged with the list of callbacks passed to the Trainer's ``callbacks`` argument.
          If a callback returned here has the same type as one or several callbacks already present in
          the Trainer's callbacks list, it will take priority and replace them.
          In addition, Lightning will make sure :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint`
          callbacks run last.
  
          Return:
              A list of callbacks which will extend the list of callbacks in the Trainer.
  
          Example::
  
              def configure_callbacks(self):
                  early_stop = EarlyStopping(monitor"val_acc", mode="max")
                  checkpoint = ModelCheckpoint(monitor="val_loss")
                  return [early_stop, checkpoint]
  
          Note:
              Certain callback methods like :meth:`~pytorch_lightning.callbacks.base.Callback.on_init_start`
              will never be invoked on the new callbacks returned here.
          """
          return []
  
      def configure_optimizers(self):
          r"""
          Choose what optimizers and learning-rate schedulers to use in your optimization.
          Normally you'd need one. But in the case of GANs or similar you might have multiple.
  
          Return:
              Any of these 6 options.
  
              - Single optimizer.
              - List or Tuple - List of optimizers.
              - Two lists - The first list has multiple optimizers, the second a list of LR schedulers (or lr_dict).
              - Dictionary, with an 'optimizer' key, and (optionally) a 'lr_scheduler'
                key whose value is a single LR scheduler or lr_dict.
              - Tuple of dictionaries as described, with an optional 'frequency' key.
              - None - Fit will run without any optimizer.
  
          Note:
              The 'frequency' value is an int corresponding to the number of sequential batches
              optimized with the specific optimizer. It should be given to none or to all of the optimizers.
              There is a difference between passing multiple optimizers in a list,
              and passing multiple optimizers in dictionaries with a frequency of 1:
              In the former case, all optimizers will operate on the given batch in each optimization step.
              In the latter, only one optimizer will operate on the given batch at every step.
  
              The lr_dict is a dictionary which contains the scheduler and its associated configuration.
              The default configuration is shown below.
  
              .. code-block:: python
  
                  {
                      'scheduler': lr_scheduler, # The LR scheduler instance (required)
                      'interval': 'epoch', # The unit of the scheduler's step size
                      'frequency': 1, # The frequency of the scheduler
                      'reduce_on_plateau': False, # For ReduceLROnPlateau scheduler
                      'monitor': 'val_loss', # Metric for ReduceLROnPlateau to monitor
                      'strict': True, # Whether to crash the training if `monitor` is not found
                      'name': None, # Custom name for LearningRateMonitor to use
                  }
  
              Only the ``scheduler`` key is required, the rest will be set to the defaults above.
  
          Examples::
  
              # most cases
              def configure_optimizers(self):
                  opt = Adam(self.parameters(), lr=1e-3)
                  return opt
  
              # multiple optimizer case (e.g.: 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]
  
              # example with step-based learning rate schedulers
              def configure_optimizers(self):
                  gen_opt = Adam(self.model_gen.parameters(), lr=0.01)
                  dis_opt = Adam(self.model_disc.parameters(), lr=0.02)
                  gen_sched = {'scheduler': ExponentialLR(gen_opt, 0.99),
                               'interval': 'step'}  # called after each training step
                  dis_sched = CosineAnnealing(discriminator_opt, T_max=10) # called every epoch
                  return [gen_opt, dis_opt], [gen_sched, dis_sched]
  
              # example with optimizer frequencies
              # see training procedure in `Improved Training of Wasserstein GANs`, Algorithm 1
              # https://arxiv.org/abs/1704.00028
              def configure_optimizers(self):
                  gen_opt = Adam(self.model_gen.parameters(), lr=0.01)
                  dis_opt = Adam(self.model_disc.parameters(), lr=0.02)
                  n_critic = 5
                  return (
                      {'optimizer': dis_opt, 'frequency': n_critic},
                      {'optimizer': gen_opt, 'frequency': 1}
                  )
  
          Note:
  
              Some things to know:
  
              - Lightning calls ``.backward()`` and ``.step()`` on each optimizer
                and learning rate scheduler as needed.
  
              - If you use 16-bit precision (``precision=16``), Lightning will automatically
                handle the optimizers for you.
  
              - If you use multiple optimizers, :meth:`training_step` will have an additional
                ``optimizer_idx`` parameter.
  
              - If you use LBFGS Lightning handles the closure function automatically for you.
  
              - If you use multiple optimizers, gradients will be calculated only
                for the parameters of current optimizer at each training step.
  
              - If you need to control how often those optimizers step or override the
                default ``.step()`` schedule, override the :meth:`optimizer_step` hook.
  
              - If you only want to call a learning rate scheduler every ``x`` step or epoch,
                or want to monitor a custom metric, you can specify these in a lr_dict:
  
                .. code-block:: python
  
                    {
                        'scheduler': lr_scheduler,
                        'interval': 'step',  # or 'epoch'
                        'monitor': 'val_f1',
                        'frequency': x,
                    }
  
          """
          rank_zero_warn("`configure_optimizers` must be implemented to be used with the Lightning Trainer")
  
      def manual_backward(self, loss: Tensor, optimizer: Optional[Optimizer] = None, *args, **kwargs) -> None:
          """
          Call this directly from your training_step when doing optimizations manually.
          By using this we can ensure that all the proper scaling when using 16-bit etc has been done for you
  
          This function forwards all args to the .backward() call as well.
  
          .. tip:: In manual mode we still automatically clip grads if Trainer(gradient_clip_val=x) is set
  
          .. tip:: In manual mode we still automatically accumulate grad over batches if
             Trainer(accumulate_grad_batches=x) is set and you use `optimizer.step()`
  
          Example::
  
              def training_step(...):
                  (opt_a, opt_b) = self.optimizers()
                  loss = ...
                  # automatically applies scaling, etc...
                  self.manual_backward(loss, opt_a)
                  opt_a.step()
          """
          if optimizer is not None:
              rank_zero_warn(
                  "`optimizer` argument to `manual_backward` is deprecated in v1.2 and will be removed in v1.4",
                  DeprecationWarning
              )
  
          # make sure we're using manual opt
          self._verify_is_manual_optimization('manual_backward')
  
          # backward
          self._running_manual_backward = True
          self.trainer.train_loop.backward(loss, optimizer=None, opt_idx=None, *args, **kwargs)
          self._running_manual_backward = False
  
      def backward(self, loss: Tensor, optimizer: Optimizer, optimizer_idx: int, *args, **kwargs) -> None:
          """
          Override backward with your own implementation if you need to.
  
          Args:
              loss: Loss is already scaled by accumulated grads
              optimizer: Current optimizer being used
              optimizer_idx: Index of the current optimizer being used
  
          Called to perform backward step.
          Feel free to override as needed.
          The loss passed in has already been scaled for accumulated gradients if requested.
  
          Example::
  
              def backward(self, loss, optimizer, optimizer_idx):
                  loss.backward()
  
          """
          if self.trainer.train_loop.automatic_optimization or self._running_manual_backward:
              loss.backward(*args, **kwargs)
  
      def toggle_optimizer(self, optimizer: Optimizer, optimizer_idx: int):
          """
          Makes sure only the gradients of the current optimizer's parameters are calculated
          in the training step to prevent dangling gradients in multiple-optimizer setup.
  
          .. note:: Only called when using multiple optimizers
  
          Override for your own behavior
  
          It works with ``untoggle_optimizer`` to make sure param_requires_grad_state is properly reset.
  
          Args:
              optimizer: Current optimizer used in training_loop
              optimizer_idx: Current optimizer idx in training_loop
          """
  
          # Iterate over all optimizer parameters to preserve their `requires_grad` information
          # in case these are pre-defined during `configure_optimizers`
          param_requires_grad_state = {}
          for opt in self.optimizers(use_pl_optimizer=False):
              for group in opt.param_groups:
                  for param in group['params']:
                      # If a param already appear in param_requires_grad_state, continue
                      if param in param_requires_grad_state:
                          continue
                      param_requires_grad_state[param] = param.requires_grad
                      param.requires_grad = False
  
          # Then iterate over the current optimizer's parameters and set its `requires_grad`
          # properties accordingly
          for group in optimizer.param_groups:
              for param in group['params']:
                  param.requires_grad = param_requires_grad_state[param]
          self._param_requires_grad_state = param_requires_grad_state
  
      def untoggle_optimizer(self, optimizer_idx: int):
          """
          .. note:: Only called when using multiple optimizers
  
          Override for your own behavior
  
          Args:
              optimizer_idx: Current optimizer idx in training_loop
          """
          for opt_idx, opt in enumerate(self.optimizers(use_pl_optimizer=False)):
              if optimizer_idx != opt_idx:
                  for group in opt.param_groups:
                      for param in group['params']:
                          if param in self._param_requires_grad_state:
                              param.requires_grad = self._param_requires_grad_state[param]
          # save memory
          del self._param_requires_grad_state
  
      def optimizer_step(
          self,
          epoch: int = None,
          batch_idx: int = None,
          optimizer: Optimizer = None,
          optimizer_idx: int = None,
          optimizer_closure: Optional[Callable] = None,
          on_tpu: bool = None,
          using_native_amp: bool = None,
          using_lbfgs: bool = None,
      ) -> None:
          r"""
          Override this method to adjust the default way the
          :class:`~pytorch_lightning.trainer.trainer.Trainer` calls each optimizer.
          By default, Lightning calls ``step()`` and ``zero_grad()`` as shown in the example
          once per optimizer.
  
          .. tip:: With ``Trainer(enable_pl_optimizer=True)``, you can use ``optimizer.step()`` directly
           and it will handle zero_grad, accumulated gradients, AMP, TPU and more automatically for you.
  
          Warning:
              If you are overriding this method, make sure that you pass the ``optimizer_closure`` parameter
              to ``optimizer.step()`` function as shown in the examples. This ensures that
              ``train_step_and_backward_closure`` is called within
              :meth:`~pytorch_lightning.trainer.training_loop.TrainLoop.run_training_batch`.
  
          Args:
              epoch: Current epoch
              batch_idx: Index of current batch
              optimizer: A PyTorch optimizer
              optimizer_idx: If you used multiple optimizers this indexes into that list.
              optimizer_closure: closure for all optimizers
              on_tpu: true if TPU backward is required
              using_native_amp: True if using native amp
              using_lbfgs: True if the matching optimizer is lbfgs
  
          Examples::
  
              # DEFAULT
              def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx,
                                 optimizer_closure, on_tpu, using_native_amp, using_lbfgs):
                  optimizer.step(closure=optimizer_closure)
  
              # Alternating schedule for optimizer steps (i.e.: GANs)
              def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx,
                                 optimizer_closure, on_tpu, using_native_amp, using_lbfgs):
                  # update generator opt every 2 steps
                  if optimizer_idx == 0:
                      if batch_idx % 2 == 0 :
                          optimizer.step(closure=optimizer_closure)
                          optimizer.zero_grad()
  
                  # update discriminator opt every 4 steps
                  if optimizer_idx == 1:
                      if batch_idx % 4 == 0 :
                          optimizer.step(closure=optimizer_closure)
                          optimizer.zero_grad()
  
                  # ...
                  # add as many optimizers as you want
  
  
          Here's another example showing how to use this for more advanced things such as
          learning rate warm-up:
  
          .. code-block:: python
  
              # learning rate warm-up
              def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx,
                                 optimizer_closure, on_tpu, using_native_amp, using_lbfgs):
                  # 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.learning_rate
  
                  # update params
                  optimizer.step(closure=optimizer_closure)
                  optimizer.zero_grad()
  
          """
          if not isinstance(optimizer, LightningOptimizer):
              # wraps into LightingOptimizer only for running step
              optimizer = LightningOptimizer._to_lightning_optimizer(optimizer, self.trainer, optimizer_idx)
          optimizer.step(closure=optimizer_closure)
  
      def optimizer_zero_grad(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int):
          optimizer.zero_grad()
  
      def tbptt_split_batch(self, batch: Tensor, split_size: int) -> list:
          r"""
          When using truncated backpropagation through time, each batch must be split along the
          time dimension. Lightning handles this by default, but for custom behavior override
          this function.
  
          Args:
              batch: Current batch
              split_size: The size of the split
  
          Return:
              List of batch splits. Each split will be passed to :meth:`training_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.
  
          Examples::
  
              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
  
          Note:
              Called in the training loop after
              :meth:`~pytorch_lightning.callbacks.base.Callback.on_batch_start`
              if :paramref:`~pytorch_lightning.trainer.Trainer.truncated_bptt_steps` > 0.
              Each returned batch split is passed separately to :meth:`training_step`.
  
          """
          time_dims = [len(x[0]) for x in batch if isinstance(x, (torch.Tensor, 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
  
      def summarize(self, mode: Optional[str] = ModelSummary.MODE_DEFAULT) -> Optional[ModelSummary]:
          model_summary = None
  
          if mode in ModelSummary.MODES:
              model_summary = ModelSummary(self, mode=mode)
              log.info("\n" + str(model_summary))
          elif mode is not None:
              raise MisconfigurationException(f"`mode` can be None, {', '.join(ModelSummary.MODES)}, got {mode}")
  
          return model_summary
  
      def freeze(self) -> None:
          r"""
          Freeze all params for inference.
  
          Example::
  
              model = MyLightningModule(...)
              model.freeze()
  
          """
          for param in self.parameters():
              param.requires_grad = False
  
          self.eval()
  
      def unfreeze(self) -> None:
          """
          Unfreeze all parameters for training.
  
          .. code-block:: python
  
              model = MyLightningModule(...)
              model.unfreeze()
  
          """
          for param in self.parameters():
              param.requires_grad = True
  
          self.train()
  
      def get_progress_bar_dict(self) -> Dict[str, Union[int, str]]:
          r"""
          Implement this to override the default items displayed in the progress bar.
          By default it includes the average loss value, split index of BPTT (if used)
          and the version of the experiment when using a logger.
  
          .. code-block::
  
              Epoch 1:   4%|▎         | 40/1095 [00:03<01:37, 10.84it/s, loss=4.501, v_num=10]
  
          Here is an example how to override the defaults:
  
          .. code-block:: python
  
              def get_progress_bar_dict(self):
                  # don't show the version number
                  items = super().get_progress_bar_dict()
                  items.pop("v_num", None)
                  return items
  
          Return:
              Dictionary with the items to be displayed in the progress bar.
          """
          # call .item() only once but store elements without graphs
          running_train_loss = self.trainer.train_loop.running_loss.mean()
          avg_training_loss = None
          if running_train_loss is not None:
              avg_training_loss = running_train_loss.cpu().item()
          elif self.trainer.train_loop.automatic_optimization:
              avg_training_loss = float('NaN')
  
          tqdm_dict = {}
          if avg_training_loss is not None:
              tqdm_dict["loss"] = f"{avg_training_loss:.3g}"
  
          if self.trainer.truncated_bptt_steps is not None:
              tqdm_dict["split_idx"] = self.trainer.split_idx
  
          if self.trainer.logger is not None and self.trainer.logger.version is not None:
              version = self.trainer.logger.version
              # show last 4 places of long version strings
              version = version[-4:] if isinstance(version, str) else version
              tqdm_dict["v_num"] = version
  
          return tqdm_dict
  
      def _verify_is_manual_optimization(self, fn_name):
          if self.trainer.train_loop.automatic_optimization:
              raise MisconfigurationException(
                  f'to use {fn_name}, please disable automatic optimization:'
                  ' set model property `automatic_optimization` as False'
              )
  
      @classmethod
      def _auto_collect_arguments(cls, frame=None) -> Tuple[Dict, Dict]:
          """
          Collect all module arguments in the current constructor and all child constructors.
          The child constructors are all the ``__init__`` methods that reach the current class through
          (chained) ``super().__init__()`` calls.
  
          Args:
              frame: instance frame
  
          Returns:
              self_arguments: arguments dictionary of the first instance
              parents_arguments: arguments dictionary of the parent's instances
          """
          if not frame:
              frame = inspect.currentframe()
  
          frame_args = collect_init_args(frame.f_back, [])
          self_arguments = frame_args[-1]
  
          # set hyper_parameters in child
          self_arguments = self_arguments
          parents_arguments = {}
  
          # add all arguments from parents
          for args in frame_args[:-1]:
              parents_arguments.update(args)
          return self_arguments, parents_arguments
  
      def save_hyperparameters(self, *args, frame=None) -> None:
          """Save all model arguments.
  
          Args:
              args: single object of `dict`, `NameSpace` or `OmegaConf`
               or string names or arguments from class `__init__`
  
          >>> class ManuallyArgsModel(LightningModule):
          ...     def __init__(self, arg1, arg2, arg3):
          ...         super().__init__()
          ...         # manually assign arguments
          ...         self.save_hyperparameters('arg1', 'arg3')
          ...     def forward(self, *args, **kwargs):
          ...         ...
          >>> model = ManuallyArgsModel(1, 'abc', 3.14)
          >>> model.hparams
          "arg1": 1
          "arg3": 3.14
  
          >>> class AutomaticArgsModel(LightningModule):
          ...     def __init__(self, arg1, arg2, arg3):
          ...         super().__init__()
          ...         # equivalent automatic
          ...         self.save_hyperparameters()
          ...     def forward(self, *args, **kwargs):
          ...         ...
          >>> model = AutomaticArgsModel(1, 'abc', 3.14)
          >>> model.hparams
          "arg1": 1
          "arg2": abc
          "arg3": 3.14
  
          >>> class SingleArgModel(LightningModule):
          ...     def __init__(self, params):
          ...         super().__init__()
          ...         # manually assign single argument
          ...         self.save_hyperparameters(params)
          ...     def forward(self, *args, **kwargs):
          ...         ...
          >>> model = SingleArgModel(Namespace(p1=1, p2='abc', p3=3.14))
          >>> model.hparams
          "p1": 1
          "p2": abc
          "p3": 3.14
          """
          if not frame:
              frame = inspect.currentframe().f_back
          init_args = get_init_args(frame)
          assert init_args, "failed to inspect the self init"
          if not args:
              # take all arguments
              hp = init_args
              self._hparams_name = "kwargs" if hp else None
          else:
              # take only listed arguments in `save_hparams`
              isx_non_str = [i for i, arg in enumerate(args) if not isinstance(arg, str)]
              if len(isx_non_str) == 1:
                  hp = args[isx_non_str[0]]
                  cand_names = [k for k, v in init_args.items() if v == hp]
                  self._hparams_name = cand_names[0] if cand_names else None
              else:
                  hp = {arg: init_args[arg] for arg in args if isinstance(arg, str)}
                  self._hparams_name = "kwargs"
  
          # `hparams` are expected here
          if hp:
              self._set_hparams(hp)
          # make deep copy so  there is not other runtime changes reflected
          self._hparams_initial = copy.deepcopy(self._hparams)
  
      def _set_hparams(self, hp: Union[dict, Namespace, str]) -> None:
          if isinstance(hp, Namespace):
              hp = vars(hp)
          if isinstance(hp, dict):
              hp = AttributeDict(hp)
          elif isinstance(hp, PRIMITIVE_TYPES):
              raise ValueError(f"Primitives {PRIMITIVE_TYPES} are not allowed.")
          elif not isinstance(hp, ALLOWED_CONFIG_TYPES):
              raise ValueError(f"Unsupported config type of {type(hp)}.")
  
          if isinstance(hp, dict) and isinstance(self.hparams, dict):
              self.hparams.update(hp)
          else:
              self._hparams = hp
  
      @torch.no_grad()
      def to_onnx(
          self,
          file_path: Union[str, Path],
          input_sample: Optional[Any] = None,
          **kwargs,
      ):
          """
          Saves the model in ONNX format
  
          Args:
              file_path: The path of the file the onnx model should be saved to.
              input_sample: An input for tracing. Default: None (Use self.example_input_array)
              **kwargs: Will be passed to torch.onnx.export function.
  
          Example:
              >>> class SimpleModel(LightningModule):
              ...     def __init__(self):
              ...         super().__init__()
              ...         self.l1 = torch.nn.Linear(in_features=64, out_features=4)
              ...
              ...     def forward(self, x):
              ...         return torch.relu(self.l1(x.view(x.size(0), -1)))
  
              >>> with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as tmpfile:
              ...     model = SimpleModel()
              ...     input_sample = torch.randn((1, 64))
              ...     model.to_onnx(tmpfile.name, input_sample, export_params=True)
              ...     os.path.isfile(tmpfile.name)
              True
          """
          mode = self.training
  
          if input_sample is None:
              if self.example_input_array is None:
                  raise ValueError(
                      "Could not export to ONNX since neither `input_sample` nor"
                      " `model.example_input_array` attribute is set."
                  )
              input_sample = self.example_input_array
  
          input_sample = self._apply_batch_transfer_handler(input_sample)
  
          if "example_outputs" not in kwargs:
              self.eval()
              kwargs["example_outputs"] = self(input_sample)
  
          torch.onnx.export(self, input_sample, file_path, **kwargs)
          self.train(mode)
  
      @torch.no_grad()
      def to_torchscript(
          self,
          file_path: Optional[Union[str, Path]] = None,
          method: Optional[str] = 'script',
          example_inputs: Optional[Any] = None,
          **kwargs,
      ) -> Union[ScriptModule, Dict[str, ScriptModule]]:
          """
          By default compiles the whole model to a :class:`~torch.jit.ScriptModule`.
          If you want to use tracing, please provided the argument `method='trace'` and make sure that either the
          example_inputs argument is provided, or the model has self.example_input_array set.
          If you would like to customize the modules that are scripted you should override this method.
          In case you want to return multiple modules, we recommend using a dictionary.
  
          Args:
              file_path: Path where to save the torchscript. Default: None (no file saved).
              method: Whether to use TorchScript's script or trace method. Default: 'script'
              example_inputs: An input to be used to do tracing when method is set to 'trace'.
                Default: None (Use self.example_input_array)
              **kwargs: Additional arguments that will be passed to the :func:`torch.jit.script` or
                :func:`torch.jit.trace` function.
  
          Note:
              - Requires the implementation of the
                :meth:`~pytorch_lightning.core.lightning.LightningModule.forward` method.
              - The exported script will be set to evaluation mode.
              - It is recommended that you install the latest supported version of PyTorch
                to use this feature without limitations. See also the :mod:`torch.jit`
                documentation for supported features.
  
          Example:
              >>> class SimpleModel(LightningModule):
              ...     def __init__(self):
              ...         super().__init__()
              ...         self.l1 = torch.nn.Linear(in_features=64, out_features=4)
              ...
              ...     def forward(self, x):
              ...         return torch.relu(self.l1(x.view(x.size(0), -1)))
              ...
              >>> model = SimpleModel()
              >>> torch.jit.save(model.to_torchscript(), "model.pt")  # doctest: +SKIP
              >>> os.path.isfile("model.pt")  # doctest: +SKIP
              >>> torch.jit.save(model.to_torchscript(file_path="model_trace.pt", method='trace', # doctest: +SKIP
              ...                                     example_inputs=torch.randn(1, 64)))  # doctest: +SKIP
              >>> os.path.isfile("model_trace.pt")  # doctest: +SKIP
              True
  
          Return:
              This LightningModule as a torchscript, regardless of whether file_path is
              defined or not.
          """
          mode = self.training
  
          if method == 'script':
              torchscript_module = torch.jit.script(self.eval(), **kwargs)
          elif method == 'trace':
              # if no example inputs are provided, try to see if model has example_input_array set
              if example_inputs is None:
                  if self.example_input_array is None:
                      raise ValueError(
                          'Choosing method=`trace` requires either `example_inputs`'
                          ' or `model.example_input_array` to be defined.'
                      )
                  example_inputs = self.example_input_array
  
              # automatically send example inputs to the right device and use trace
              example_inputs = self._apply_batch_transfer_handler(example_inputs)
              torchscript_module = torch.jit.trace(func=self.eval(), example_inputs=example_inputs, **kwargs)
          else:
              raise ValueError(f"The 'method' parameter only supports 'script' or 'trace', but value given was: {method}")
  
          self.train(mode)
  
          if file_path is not None:
              torch.jit.save(torchscript_module, file_path)
  
          return torchscript_module
  
      @property
      def hparams(self) -> Union[AttributeDict, dict, Namespace]:
          if not hasattr(self, "_hparams"):
              self._hparams = AttributeDict()
          return self._hparams
  
      @property
      def hparams_initial(self) -> AttributeDict:
          if not hasattr(self, "_hparams_initial"):
              return AttributeDict()
          # prevent any change
          return copy.deepcopy(self._hparams_initial)
  
      @hparams.setter
      def hparams(self, hp: Union[dict, Namespace, Any]):
          # TODO: remove this method in v1.3.0.
          rank_zero_warn(
              "The setter for self.hparams in LightningModule is deprecated since v1.1.0 and will be"
              " removed in v1.3.0. Replace the assignment `self.hparams = hparams` with "
              " `self.save_hyperparameters()`.", DeprecationWarning
          )
          hparams_assignment_name = self.__get_hparams_assignment_variable()
          self._hparams_name = hparams_assignment_name
          self._set_hparams(hp)
          # this resolves case when user does not uses `save_hyperparameters` and do hard assignement in init
          if not hasattr(self, "_hparams_initial"):
              self._hparams_initial = copy.deepcopy(self._hparams)
  
      def __get_hparams_assignment_variable(self):
          """
          looks at the code of the class to figure out what the user named self.hparams
          this only happens when the user explicitly sets self.hparams
          """
          try:
              class_code = inspect.getsource(self.__class__)
              lines = class_code.split("\n")
              for line in lines:
                  line = re.sub(r"\s+", "", line, flags=re.UNICODE)
                  if ".hparams=" in line:
                      return line.split("=")[1]
          # todo: specify the possible exception
          except Exception:
              return "hparams"
  
          return None
  
      @property
      def model_size(self) -> float:
          # todo: think about better way without need to dump model to drive
          tmp_name = f"{uuid.uuid4().hex}.pt"
          torch.save(self.state_dict(), tmp_name)
          size_mb = os.path.getsize(tmp_name) / 1e6
          os.remove(tmp_name)
          return size_mb
  