code: def __init__(self, config: TimesformerConfig) -> None:
      super().__init__()
      self.dense = nn.Linear(
          config.intermediate_size, config.hidden_size
      )
      self.dropout = nn.Dropout(config.hidden_dropout_prob)
  
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 TimesformerOutput object from inherited base classes: nn with example: obj = TimesformerOutput(config)