code: def __init__(self, config):
      super().__init__(config)
      self.config = config
  
      self.embeddings = TimesformerEmbeddings(config)
      self.encoder = TimesformerEncoder(config)
  
      self.layernorm = nn.LayerNorm(
          config.hidden_size, eps=config.layer_norm_eps
      )
  
      # Initialize weights and apply final processing
      self.post_init()
  
imports: import collections
  from typing import Optional, Tuple, Union
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_timesformer import TimesformerConfig
label_desc: Use image node_id 1 to create a new TimesformerModel object from inherited base classes: TimesformerPreTrainedModel with example: obj = TimesformerModel(config)