code: # 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.
  
  import collections
  import inspect
  import os
  import re
  import tempfile
  from abc import ABC, abstractmethod
  from argparse import Namespace
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
  
  import torch
  import torch.distributed as torch_distrib
  from torch import Tensor
  from torch.nn import Module
  from torch.nn.parallel import DistributedDataParallel
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
  
  from pytorch_lightning import _logger as log
  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 ALLOWED_CONFIG_TYPES, PRIMITIVE_TYPES, ModelIO
  from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
  from pytorch_lightning.utilities import rank_zero_warn
  from pytorch_lightning.utilities.device_dtype_mixin import DeviceDtypeModuleMixin
  from pytorch_lightning.utilities.parsing import AttributeDict, collect_init_args, get_init_args
  from pytorch_lightning.core.step_result import TrainResult, EvalResult
  
  try:
      import torch_xla.core.xla_model as xm
  except ImportError:
      XLA_AVAILABLE = False
  else:
      XLA_AVAILABLE = True
  
  
  class LightningModule(ABC, DeviceDtypeModuleMixin, GradInformation, ModelIO, ModelHooks, Module):
      def __init__(self, *args, **kwargs):
          super().__init__(*args, **kwargs)
  
          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 = {}
  
          #: Pointer to the trainer object
          self.trainer = None
  
          #: Pointer to the logger object
          self.logger = None
  
          #: True if using dp
          self.use_dp = False
  
          #: True if using ddp
          self.use_ddp = False
  
          #: True if using ddp2
          self.use_ddp2 = False
  
          # True if on tpu
          self.use_tpu = False
  
          #: 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
  
      @property
      def example_input_array(self) -> Any:
          return self._example_input_array
  
      @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'
  
      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. Will be passed to Python's built-in print function.
              **kwargs: Will be passed to Python's built-in print function.
  
          Example:
  
              .. code-block:: python
  
                  def forward(self, x):
                      self.print(x, 'in forward')
  
          """
          if self.trainer.is_global_zero:
              print(*args, **kwargs)
  
      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:
              .. code-block:: python
  
                  # 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
  
          """
  
      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:
              :class:`~pytorch_lightning.core.step_result.TrainResult`
  
              .. note:: :class:`~pytorch_lightning.core.step_result.TrainResult` is simply a Dict with convenient
                  functions for logging, distributed sync and error checking.
  
          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
  
                  # implement your own
                  out = self(x)
                  loss = self.loss(out, x)
  
                  # TrainResult auto-detaches the loss after the optimization steps are complete
                  result = pl.TrainResult(minimize=loss)
  
          The return object :class:`~pytorch_lightning.core.step_result.TrainResult` controls where to log,
          when to log (step or epoch) and syncing with multiple GPUs.
  
          .. code-block:: python
  
              # log to progress bar and logger
              result.log('train_loss', loss, prog_bar=True, logger=True)
  
              # sync metric value across GPUs in distributed training
              result.log('train_loss_2', loss, sync_dist=True)
  
              # log to progress bar as well
              result.log('train_loss_2', loss, prog_bar=True)
  
              # assign arbitrary values
              result.predictions = predictions
              result.some_value = 'some_value'
  
          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)
                  ...
  
                  # TrainResult auto-detaches hiddens
                  result = pl.TrainResult(minimize=loss, hiddens=hiddens)
                  return result
  
          Notes:
              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:
              :class:`~pytorch_lightning.core.step_result.TrainResult`
  
              .. note:: :class:`~pytorch_lightning.core.step_result.TrainResult` is simply a Dict with convenient
                  functions for logging, distributed sync and error checking.
  
          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 pl.TrainResult(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(x)
                  result = pl.TrainResult()
                  result.out = out
  
              def training_step_end(self, training_step_outputs):
                  # this out is now the full size of the batch
                  all_outs = training_step_outputs.out
  
                  # this softmax now uses the full batch
                  loss = nce_loss(all_outs)
                  result = pl.TrainResult(loss)
                  return result
  
          See Also:
              See the :ref:`multi-gpu-training` guide for more details.
          """
  
      def training_epoch_end(
              self, outputs: Union[TrainResult, List[TrainResult]]
      ):
          """
          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:
              :class:`~pytorch_lightning.core.step_result.TrainResult`
  
          .. note:: :class:`~pytorch_lightning.core.step_result.TrainResult` is simply a Dict with convenient
              functions for logging, distributed sync and error checking.
  
          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, outputs):
                  epoch_result = pl.TrainResult()
                  for train_result in outputs:
                      all_losses = train_result.minimize
                      # do something with all losses
                  return results
          """
  
      def validation_step(self, *args, **kwargs) -> EvalResult:
          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(train_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 datasets used)
  
          Return:
              :class:`~pytorch_lightning.core.step_result.TrainResult`
  
          .. 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:
              .. code-block:: python
  
                  # 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!
                      result = pl.EvalResult(checkpoint_on=loss)
                      result.log_dict({'val_loss': loss, 'val_acc': val_acc})
                      return result
  
              If you pass in multiple val datasets, validation_step will have an additional argument.
  
              .. code-block:: python
  
                  # CASE 2: multiple validation datasets
                  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) -> EvalResult:
          """
          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:
              :class:`~pytorch_lightning.core.step_result.TrainResult`
  
          .. 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(x)
                  loss = self.softmax(out)
                  loss = nce_loss(loss)
                  result = pl.EvalResult()
                  result.log('val_loss', loss)
                  return result
  
              # --------------
              # 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)
                  result = pl.EvalResult()
                  result.out = out
                  return result
  
              def validation_epoch_end(self, output_results):
                  # this out is now the full size of the batch
                  all_val_step_outs = output_results.out
                  loss = nce_loss(all_val_step_outs)
  
                  result = pl.EvalResult(checkpoint_on=loss)
                  result.log('val_loss', loss)
                  return result
  
          See Also:
              See the :ref:`multi-gpu-training` guide for more details.
          """
  
      def validation_end(self, outputs):
          """
          Warnings:
              Deprecated in v0.7.0. Use :meth:`validation_epoch_end` instead.
              Will be removed in 1.0.0.
          """
  
      def validation_epoch_end(
              self, outputs: Union[EvalResult, List[EvalResult]]
      ) -> EvalResult:
          """
          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:
              :class:`~pytorch_lightning.core.step_result.TrainResult`
  
          Note:
              If you didn't define a :meth:`validation_step`, this won't be called.
  
          - The outputs here are strictly for logging or progress bar.
          - If you don't need to display anything, don't return anything.
  
          Examples:
              With a single dataloader:
  
              .. code-block:: python
  
                  def validation_epoch_end(self, val_step_outputs):
                      # do something with the outputs of all val batches
                      all_val_preds = val_step_outputs.predictions
  
                      val_step_outputs.some_result = calc_all_results(all_val_preds)
                      return val_step_outputs
  
              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
  
                      result = pl.EvalResult()
                      result.log('final_metric', final_value)
                      return result
          """
  
      def test_step(self, *args, **kwargs) -> EvalResult:
          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 datasets used).
  
          Return:
              :class:`~pytorch_lightning.core.step_result.TrainResult`
  
          .. 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:
              .. code-block:: python
  
                  # 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!
                      result = pl.EvalResult(checkpoint_on=loss)
                      result.log_dict({'test_loss': loss, 'test_acc': test_acc})
                      return resultt
  
              If you pass in multiple validation datasets, :meth:`test_step` will have an additional
              argument.
  
              .. code-block:: python
  
                  # CASE 2: multiple test datasets
                  def test_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:`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) -> EvalResult:
          """
          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:
              :class:`~pytorch_lightning.core.step_result.TrainResult`
  
          .. 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)
                  loss = nce_loss(loss)
                  result = pl.EvalResult()
                  result.log('test_loss', loss)
                  return result
  
              # --------------
              # 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(x)
                  result = pl.EvalResult()
                  result.out = out
                  return result
  
              def test_epoch_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)
  
                  result = pl.EvalResult(checkpoint_on=loss)
                  result.log('test_loss', loss)
                  return result
  
          See Also:
              See the :ref:`multi-gpu-training` guide for more details.
          """
  
      def test_end(self, outputs):
          """
          Warnings:
               Deprecated in v0.7.0. Use :meth:`test_epoch_end` instead.
               Will be removed in 1.0.0.
          """
  
      def test_epoch_end(
              self, outputs: Union[EvalResult, List[EvalResult]]
      ) -> EvalResult:
  
          """
          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:
              :class:`~pytorch_lightning.core.step_result.TrainResult`
  
          Note:
              If you didn't define a :meth:`test_step`, this won't be called.
  
          - The outputs here are strictly for logging or progress bar.
          - If you don't need to display anything, don't return anything.
  
          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
  
                      test_step_outputs.some_result = calc_all_results(all_test_preds)
                      return test_step_outputs
  
              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):
                      for dataloader_output_result in outputs:
                          dataloader_outs = dataloader_output_result.dataloader_i_outputs
  
                      result = pl.EvalResult()
                      result.log('final_metric', final_value)
                      return results
          """
  
      def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) -> DistributedDataParallel:
          r"""
          Override to init DDP in your own way or with your own wrapper.
          The only requirements are 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``.
  
          Args:
              model: the :class:`LightningModule` currently being optimized.
              device_ids: the list of GPU ids.
  
          Return:
              DDP wrapped model
  
          Examples:
              .. code-block:: python
  
                  # default implementation used in Trainer
                  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_slurm_connection(self) -> None:
          """"""
          """
          Sets up environment variables necessary for pytorch distributed communications
          based on slurm environment.
          """
          # 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:
              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 = '127.0.0.1'
  
          root_node = self.trainer.resolve_root_node_address(root_node)
          os.environ['MASTER_ADDR'] = root_node
  
      def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool = True) -> None:
          """
          Override to define your custom way of setting up a distributed environment.
  
          Lightning's implementation uses env:// init by default and sets the first node as root
          for SLURM managed cluster.
  
          Args:
              global_rank: The global process idx.
              world_size: Number of GPUs being use across all nodes. (num_nodes * num_gpus).
              is_slurm_managing_tasks: is cluster managed by SLURM.
  
          """
          if is_slurm_managing_tasks:
              self._init_slurm_connection()
  
          if 'MASTER_ADDR' not in os.environ:
              rank_zero_warn("MASTER_ADDR environment variable is not defined. Set as localhost")
              os.environ['MASTER_ADDR'] = '127.0.0.1'
          log.debug(f"MASTER_ADDR: {os.environ['MASTER_ADDR']}")
  
          if 'MASTER_PORT' not in os.environ:
              rank_zero_warn("MASTER_PORT environment variable is not defined. Set as 12910")
              os.environ['MASTER_PORT'] = '12910'
          log.debug(f"MASTER_PORT: {os.environ['MASTER_PORT']}")
  
          if 'WORLD_SIZE' in os.environ and int(os.environ['WORLD_SIZE']) != world_size:
              rank_zero_warn(
                  f"WORLD_SIZE environment variable ({os.environ['WORLD_SIZE']}) "
                  f"is not equal to the computed world size ({world_size}). Ignored."
              )
  
          torch_backend = "nccl" if self.trainer.on_gpu else "gloo"
          log.info(f"initializing ddp: GLOBAL_RANK: {global_rank}, MEMBER: {global_rank+1}/{world_size}")
          torch_distrib.init_process_group(torch_backend, rank=global_rank, world_size=world_size)
  
      def configure_sync_batchnorm(self, model: 'LightningModule') -> 'LightningModule':
          """
          Add global batchnorm for a model spread across multiple GPUs and nodes.
  
          Override to synchronize batchnorm between specific process groups instead
          of the whole world or use a different sync_bn like `apex`'s version.
  
          Args:
              model: pointer to current :class:`LightningModule`.
  
          Return:
              LightningModule with batchnorm layers synchronized between process groups
          """
          model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model, process_group=None)
  
          return model
  
      def configure_apex(
          self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str
      ) -> Tuple['LightningModule', List[Optimizer]]:
          r"""
          Override to init AMP your own way.
          Must return a model and list of optimizers.
  
          Args:
              amp: pointer to amp library object.
              model: pointer to current :class:`LightningModule`.
              optimizers: list of optimizers passed in :meth:`configure_optimizers`.
              amp_level: AMP mode chosen ('O1', 'O2', etc...)
  
          Return:
              Apex wrapped model and optimizers
  
          Examples:
              .. code-block:: python
  
                  # Default implementation used by Trainer.
                  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,
      ) -> Optional[Union[Optimizer, Sequence[Optimizer], Dict, Sequence[Dict], Tuple[List, List]]]:
          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 which 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 scheduler and its associated configuration.
              It has five keys. The default configuration is shown below.
  
              .. code-block:: python
  
                  {
                      'scheduler': lr_scheduler, # The LR schduler
                      '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 to monitor
                  }
  
              If user only provides LR schedulers, then their configuration will set to default as shown above.
  
          Examples:
              .. code-block:: python
  
                  # 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 optimizer_step(
          self,
          epoch: int,
          batch_idx: int,
          optimizer: Optimizer,
          optimizer_idx: int,
          second_order_closure: Optional[Callable] = None,
          on_tpu: bool = False,
          using_native_amp: bool = False,
          using_lbfgs: bool = False,
      ) -> 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.
  
          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.
              second_order_closure: closure for second order methods
              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:
              .. code-block:: python
  
                  # DEFAULT
                  def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,
                                     second_order_closure, on_tpu, using_native_amp, using_lbfgs):
                      optimizer.step()
  
                  # Alternating schedule for optimizer steps (i.e.: GANs)
                  def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,
                                     second_order_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()
                              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
  
  
              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, current_epoch, batch_idx, optimizer,
                                      optimizer_idx, second_order_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()
                      optimizer.zero_grad()
  
          Note:
              If you also override the :meth:`~pytorch_lightning.core.hooks.ModelHooks.on_before_zero_grad`
              model hook don't forget to add the call to it before ``optimizer.zero_grad()`` yourself.
  
          """
          if on_tpu:
              xm.optimizer_step(optimizer)
          elif using_native_amp:
              self.trainer.scaler.step(optimizer)
          elif using_lbfgs:
              optimizer.step(second_order_closure)
          else:
              optimizer.step()
  
      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:
              .. 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
  
          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 prepare_data(self) -> None:
          """
          Use this to download and prepare data.
  
          .. warning:: DO NOT set state to the model (use `setup` instead)
              since this is NOT called on every GPU in DDP/TPU
  
          Example::
  
              def prepare_data(self):
                  # good
                  download_data()
                  tokenize()
                  etc()
  
                  # bad
                  self.split = data_split
                  self.some_state = some_other_state()
  
          In DDP prepare_data can be called in two ways (using Trainer(prepare_data_per_node)):
  
          1. Once per node. This is the default and is only called on LOCAL_RANK=0.
          2. Once in total. Only called on GLOBAL_RANK=0.
  
          Example::
  
              # DEFAULT
              # called once per node on LOCAL_RANK=0 of that node
              Trainer(prepare_data_per_node=True)
  
              # call on GLOBAL_RANK=0 (great for shared file systems)
              Trainer(prepare_data_per_node=False)
  
          This is called before requesting the dataloaders:
  
          .. code-block:: python
  
              model.prepare_data()
                  if ddp/tpu: init()
              model.setup(stage)
              model.train_dataloader()
              model.val_dataloader()
              model.test_dataloader()
          """
  
      def train_dataloader(self) -> DataLoader:
          """
          Implement a PyTorch DataLoader for training.
  
          Return:
              Single PyTorch :class:`~torch.utils.data.DataLoader`.
  
          The dataloader you return will not be called every epoch unless you set
          :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.
  
          For data processing use the following pattern:
  
              - download in :meth:`prepare_data`
              - process and split in :meth:`setup`
  
          However, the above are only necessary for distributed processing.
  
          .. warning:: do not assign state in prepare_data
  
          - :meth:`~pytorch_lightning.trainer.Trainer.fit`
          - ...
          - :meth:`prepare_data`
          - :meth:`setup`
          - :meth:`train_dataloader`
  
          Note:
              Lightning adds the correct sampler for distributed and arbitrary hardware.
              There is no need to set it yourself.
  
          Example:
              .. code-block:: python
  
                  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.batch_size,
                          shuffle=True
                      )
                      return loader
  
          """
          rank_zero_warn('`train_dataloader` must be implemented to be used with the Lightning Trainer')
  
      def tng_dataloader(self):  # todo: remove in v1.0.0
          """
          Warnings:
              Deprecated in v0.5.0. Use :meth:`train_dataloader` instead. Will be removed in 1.0.0.
          """
          output = self.train_dataloader()
          rank_zero_warn(
              "`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0."
              " and this method will be removed in v1.0.0",
              DeprecationWarning,
          )
          return output
  
      def test_dataloader(self) -> Union[DataLoader, List[DataLoader]]:
          r"""
          Implement one or multiple PyTorch DataLoaders for testing.
  
          The dataloader you return will not be called every epoch unless you set
          :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.
  
          For data processing use the following pattern:
  
              - download in :meth:`prepare_data`
              - process and split in :meth:`setup`
  
          However, the above are only necessary for distributed processing.
  
          .. warning:: do not assign state in prepare_data
  
  
          - :meth:`~pytorch_lightning.trainer.Trainer.fit`
          - ...
          - :meth:`prepare_data`
          - :meth:`setup`
          - :meth:`train_dataloader`
          - :meth:`val_dataloader`
          - :meth:`test_dataloader`
  
          Note:
              Lightning adds the correct sampler for distributed and arbitrary hardware.
              There is no need to set it yourself.
  
          Return:
              Single or multiple PyTorch DataLoaders.
  
          Example:
              .. code-block:: python
  
                  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.batch_size,
                          shuffle=False
                      )
  
                      return loader
  
                  # can also return multiple dataloaders
                  def test_dataloader(self):
                      return [loader_a, loader_b, ..., loader_n]
  
          Note:
              If you don't need a test dataset and a :meth:`test_step`, you don't need to implement
              this method.
  
          Note:
              In the case where you return multiple test dataloaders, the :meth:`test_step`
              will have an argument ``dataloader_idx`` which matches the order here.
          """
  
      def val_dataloader(self) -> Union[DataLoader, List[DataLoader]]:
          r"""
          Implement one or multiple PyTorch DataLoaders for validation.
  
          The dataloader you return will not be called every epoch unless you set
          :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.
  
          It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.
  
          - :meth:`~pytorch_lightning.trainer.Trainer.fit`
          - ...
          - :meth:`prepare_data`
          - :meth:`train_dataloader`
          - :meth:`val_dataloader`
          - :meth:`test_dataloader`
  
          Note:
              Lightning adds the correct sampler for distributed and arbitrary hardware
              There is no need to set it yourself.
  
          Return:
              Single or multiple PyTorch DataLoaders.
  
          Examples:
              .. code-block:: python
  
                  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.batch_size,
                          shuffle=False
                      )
  
                      return loader
  
                  # can also return multiple dataloaders
                  def val_dataloader(self):
                      return [loader_a, loader_b, ..., loader_n]
  
          Note:
              If you don't need a validation dataset and a :meth:`validation_step`, you don't need to
              implement this method.
  
          Note:
              In the case where you return multiple validation dataloaders, the :meth:`validation_step`
              will have an argument ``dataloader_idx`` which matches the order here.
          """
  
      def summarize(self, mode: str = ModelSummary.MODE_DEFAULT) -> ModelSummary:
          model_summary = ModelSummary(self, mode=mode)
          log.info('\n' + str(model_summary))
          return model_summary
  
      def freeze(self) -> None:
          r"""
          Freeze all params for inference.
  
          Example:
              .. code-block:: python
  
                  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 on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
          r"""
          Called by Lightning to restore your model.
          If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this.
  
          Args:
              checkpoint: Loaded checkpoint
  
  
          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']
  
          Note:
              Lightning auto-restores global step, epoch, and train state including amp scaling.
              There is no need for you to restore anything regarding training.
          """
  
      def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
          r"""
          Called by Lightning when saving a checkpoint to give you a chance to store anything
          else you might want to save.
  
          Args:
              checkpoint: Checkpoint to be saved
  
          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
  
          Note:
              Lightning saves all aspects of training (epoch, global step, etc...)
              including amp scaling.
              There is no need for you to store anything about training.
  
          """
  
      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.running_loss.mean()
          avg_training_loss = running_train_loss.cpu().item() if running_train_loss is not None else float('NaN')
          tqdm_dict = {'loss': '{:.3f}'.format(avg_training_loss)}
  
          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 get_tqdm_dict(self) -> Dict[str, Union[int, str]]:
          """
          Additional items to be displayed in the progress bar.
  
          Return:
              Dictionary with the items to be displayed in the progress bar.
  
          Warning:
              Deprecated since v0.7.3.
              Use :meth:`get_progress_bar_dict` instead.
          """
          rank_zero_warn(
              "`get_tqdm_dict` was renamed to `get_progress_bar_dict` in v0.7.3"
              " and this method will be removed in v1.0.0",
              DeprecationWarning,
          )
          return self.get_progress_bar_dict()
  
      @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 module_arguments 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 argumenst from class `__init__`
  
          >>> from collections import OrderedDict
          >>> 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:
              hp = init_args
              self._hparams_name = 'kwargs' if hp else None
          else:
              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)
  
      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
  
      def to_onnx(self, file_path: str, input_sample: Optional[Tensor] = None, **kwargs):
          """Saves the model in ONNX format
  
          Args:
              file_path: The path of the file the model should be saved to.
              input_sample: A sample of an input tensor for tracing.
              **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
          """
  
          if isinstance(input_sample, Tensor):
              input_data = input_sample
          elif self.example_input_array is not None:
              input_data = self.example_input_array
          else:
              if input_sample is not None:
                  raise ValueError(f'Received `input_sample` of type {type(input_sample)}. Expected type is `Tensor`')
              else:
                  raise ValueError('Could not export to ONNX since neither `input_sample` nor'
                                   ' `model.example_input_array` attribute is set.')
          input_data = input_data.to(self.device)
          if 'example_outputs' not in kwargs:
              self.eval()
              with torch.no_grad():
                  kwargs['example_outputs'] = self(input_data)
  
          torch.onnx.export(self, input_data, file_path, **kwargs)
  
      @property
      def hparams(self) -> Union[AttributeDict, str]:
          if not hasattr(self, '_hparams'):
              self._hparams = AttributeDict()
          return self._hparams
  
      @hparams.setter
      def hparams(self, hp: Union[dict, Namespace, Any]):
          hparams_assignment_name = self.__get_hparams_assignment_variable()
          self._hparams_name = hparams_assignment_name
          self._set_hparams(hp)
  
      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]
          except Exception as e:
              return 'hparams'
  
          return None
  
apis: torch.distributed.init_process_group torch.no_grad torch.onnx.export torch.nn.SyncBatchNorm.convert_sync_batchnorm