class_docstr: Multimodal Transformers From Pretrained BERT Weights"
docstr: Args:
      num_language_layers: number of language transformer layers.
      num_vision_layers: number of vision transformer layers.
      bert_config: configuration for bert language transformer encoder.
docstr_tok: ["Args",":","num_language_layers",":","number","of","language","transformer","layers",".","num_vision_layers",":","number","of","vision","transformer","layers",".","bert_config",":","configuration","for","bert","language","transformer","encoder","."]
code: def __init__(
      self,
      num_language_layers: int,
      num_vision_layers: int,
      num_mixed_layers: int,
      bert_config: dict,
  ) -> None:
      """
      Args:
          num_language_layers: number of language transformer layers.
          num_vision_layers: number of vision transformer layers.
          bert_config: configuration for bert language transformer encoder.
  
      """
      super().__init__()
      self.config = type("obj", (object,), bert_config)
      self.embeddings = BertEmbeddings(self.config)
      self.language_encoder = nn.ModuleList(
          [BertLayer(self.config) for _ in range(num_language_layers)]
      )
      self.vision_encoder = nn.ModuleList(
          [BertLayer(self.config) for _ in range(num_vision_layers)]
      )
      self.mixed_encoder = nn.ModuleList(
          [BertMixedLayer(self.config) for _ in range(num_mixed_layers)]
      )
      self.apply(self.init_bert_weights)
  
imports: from __future__ import annotations
  import math
  from collections.abc import Sequence
  import torch
  from torch import nn
  from monai.config.type_definitions import PathLike
  from monai.utils import optional_import
label_desc: Use image node_id 1 to create a new MultiModal object from inherited base classes: BertPreTrainedModel with example: obj = MultiModal(num_language_layers, num_vision_layers, num_mixed_layers, bert_config)