code: def __init__(self, config):
      super().__init__(config)
      self.num_labels = config.num_labels
      self.regnet = RegNetModel(config)
      # classification head
      self.classifier = nn.Sequential(
          nn.Flatten(),
          nn.Linear(config.hidden_sizes[-1], config.num_labels)
          if config.num_labels > 0
          else nn.Identity(),
      )
      # initialize weights and apply final processing
      self.post_init()
  
imports: from typing import Optional
  import torch
  import torch.utils.checkpoint
  from torch import Tensor, nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_outputs import BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_regnet import RegNetConfig
label_desc: Use image node_id 1 to create a new RegNetForImageClassification object from inherited base classes: RegNetPreTrainedModel with example: obj = RegNetForImageClassification(config)