code: def __init__(self, automatic_optimization=True):
      super().__init__()
      self._automatic_optimization = automatic_optimization
      self._training_step_generator = None
      self._reporter = pl.loggers.base.DummyExperiment()
      # For the generator API
      self._verified_steps = False
      # For summary_writer property
      self._summary_writer_logger = None
      self._summary_writer = None
      # To enable incremental training
      self.register_buffer("_next_stopping_epoch", None)
      self.register_buffer("_cleanly_stopped", None)
      self._next_stopping_epoch = torch.tensor([-1]).int()
      self._cleanly_stopped = torch.ones(1)
      self._setup_input_type()
      # Counters
      self.train_batches_processed_this_epoch = 0
      self.val_batches_processed_this_epoch = 0
      self.test_batches_processed_this_epoch = 0
      self.all_batches_processed = 0
  
imports: import inspect
  import logging
  import pytorch_lightning
  import torch
  from reagent.core.tensorboardX import SummaryWriterContext
  from reagent.core.utils import lazy_property
  from typing_extensions import final
label_desc: Use image node_id 1 to create a new ReAgentLightningModule object from inherited base classes: pl with example: obj = ReAgentLightningModule(automatic_optimization)