code: def __init__(self, config, add_pooling_layer=True):
      super().__init__(config)
      self.config = config
  
      self.embeddings = VivitEmbeddings(config)
      self.encoder = VivitEncoder(config)
  
      self.layernorm = nn.LayerNorm(
          config.hidden_size, eps=config.layer_norm_eps
      )
      self.pooler = VivitPooler(config) if add_pooling_layer else None
  
      # Initialize weights and apply final processing
      self.post_init()
  
imports: import math
  from typing import Optional, Set, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_vivit import VivitConfig
label_desc: Use image node_id 1 to create a new VivitModel object from inherited base classes: VivitPreTrainedModel with example: obj = VivitModel(config, add_pooling_layer)