code: def __init__(self, config):
      super().__init__(config)
      super()._init_backbone(config)
  
      self.embeddings = ConvNextV2Embeddings(config)
      self.encoder = ConvNextV2Encoder(config)
      self.num_features = [config.hidden_sizes[0]] + config.hidden_sizes
  
      # Add layer norms to hidden states of out_features
      hidden_states_norms = {}
      for stage, num_channels in zip(self._out_features, self.channels):
          hidden_states_norms[stage] = ConvNextV2LayerNorm(
              num_channels, data_format="channels_first"
          )
      self.hidden_states_norms = nn.ModuleDict(hidden_states_norms)
  
      # 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 ConvNextV2Backbone object from inherited base classes: ConvNextV2PreTrainedModel, BackboneMixin with example: obj = ConvNextV2Backbone(config)