class_docstr: All trainable embeddings must inherit from BaseEmbedding.
class_docstr_tok: ["All","trainable","embeddings","must","inherit","from","BaseEmbedding","."]
docstr: Constructor
  
  Args:
      model: (torch.nn.Module)
      criterion: (torch.nn.Module)
      optimizer: (torch.optim.Optimizer)
      dataloader: (torch.utils.data.DataLoader)
docstr_tok: ["Constructor","Args",":","model",":","(","torch.nn.Module",")","criterion",":","(","torch.nn.Module",")","optimizer",":","(","torch.optim.Optimizer",")","dataloader",":","(","torch.utils.data.DataLoader",")"]
code: def __init__(
      self,
      model: BenchmarkModule,
      criterion: Module,
      optimizer: Optimizer,
      dataloader: DataLoader[LightlyDataset],
      scheduler: Optional[_LRScheduler] = None,
  ) -> None:
      """Constructor
  
      Args:
          model: (torch.nn.Module)
          criterion: (torch.nn.Module)
          optimizer: (torch.optim.Optimizer)
          dataloader: (torch.utils.data.DataLoader)
  
      """
  
      super(BaseEmbedding, self).__init__()
      self.model = model
      self.criterion = criterion
      self.optimizer = optimizer
      self.dataloader = dataloader
      self.scheduler = scheduler
      self.checkpoint: Optional[str] = None
      self.cwd = os.getcwd()
  
imports: import copy
  import os
  from typing import Any, List, Optional, Sequence, Tuple, Union
  import omegaconf
  from omegaconf import DictConfig
  from pytorch_lightning import LightningModule, Trainer
  from pytorch_lightning.callbacks.callback import Callback
  from torch import Tensor
  from torch.nn import Module
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import _LRScheduler
  from torch.utils.data import DataLoader
  from lightly.data.dataset import LightlyDataset
  from lightly.embedding import callbacks
  from lightly.utils.benchmarking import BenchmarkModule
label_desc: Use image node_id 1 to create a new BaseEmbedding object from inherited base classes: LightningModule with example: obj = BaseEmbedding(model, criterion, optimizer, dataloader, scheduler)