class_docstr: Experiment configuration. Experiment Tracking with WandB and Tensorboard
  Args:
      project_name (str): The name of the project under which all runs will be logged. For Tensorboard
              this defines the folder under which the logs will be saved and for W&B it defines the project name
  
      run_name (Optional[str]): The name of the run; a specific identifier to recognize the run. If left
              blank, will be assigned an auto-generated name
  
      exp_watch (Optional[str]): The level of logging required.  Can be `gradients`, `parameters`, `all`
              or `None`. Defaults to None. Choices are: [`gradients`,`parameters`,`all`,`None`].
  
      log_target (str): Determines where logging happens - Tensorboard or W&B. Choices are:
              [`wandb`,`tensorboard`].
  
      log_logits (bool): Turn this on to log the logits as a histogram in W&B
  
      exp_log_freq (int): step count between logging of gradients and parameters.
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code: def __post_init__(self):
      _validate_choices(self)
      if self.log_target == "wandb":
          try:
              import wandb  # noqa: F401
          except ImportError:
              raise ImportError(
                  "No W&B installation detected. `pip install wandb` to install W&B if you set log_target as `wandb`"
              )
  
imports: import os
  import re
  from dataclasses import MISSING, dataclass, field
  from typing import Any, Dict, Iterable, List, Optional
  from omegaconf import OmegaConf
  from pytorch_tabular.models.common import heads
  from pytorch_tabular.utils import get_logger
label_desc: Use image node_id 1 for calling the ExperimentConfig obj's underlying member method code with example usage: obj.__post_init__() without return types