code: def __init__(self, config):
      super().__init__(config)
  
      self.num_labels = config.num_labels
      self.convnextv2 = ConvNextV2Model(config)
  
      # Classifier head
      self.classifier = (
          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, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BackboneOutput, BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from ...utils.backbone_utils import BackboneMixin
  from .configuration_convnextv2 import ConvNextV2Config
label_desc: Use image node_id 1 to create a new ConvNextV2ForImageClassification object from inherited base classes: ConvNextV2PreTrainedModel with example: obj = ConvNextV2ForImageClassification(config)