class_docstr: Saves model topology, losses & metrics
class_docstr_tok: ["Saves","model","topology",",","losses","&","metrics"]
code: def __init__(
      self,
      log: str = None,  # What to log (can be `gradients`, `parameters`, `all` or None)
      log_preds: bool = True,  # Whether to log model predictions on a `wandb.Table`
      log_preds_every_epoch: bool = False,  # Whether to log predictions every epoch or at the end
      log_model: bool = False,  # Whether to save the model checkpoint to a `wandb.Artifact`
      model_name: str = None,  # The name of the `model_name` to save, overrides `SaveModelCallback`
      log_dataset: bool = False,  # Whether to log the dataset to a `wandb.Artifact`
      dataset_name: str = None,  # A name to log the dataset with
      valid_dl: TfmdDL = None,  # If `log_preds=True`, then the samples will be drawn from `valid_dl`
      n_preds: int = 36,  # How many samples to log predictions
      seed: int = 12345,  # The seed of the samples drawn
      reorder=True,
  ):
      store_attr()
  
imports: from __future__ import annotations
  from ..basics import *
  from .progress import *
  from ..text.data import TensorText
  from ..tabular.all import TabularDataLoaders, Tabular
  from .hook import total_params
  from .tracker import SaveModelCallback
  import wandb
label_desc: Use image node_id 1 to create a new WandbCallback object from inherited base classes: Callback with example: obj = WandbCallback(log, log_preds, log_preds_every_epoch, log_model, model_name, log_dataset, dataset_name, valid_dl, n_preds, seed, reorder)