class_docstr: This is the configuration class to store the configuration of a [`LayoutLMv2Model`]. It is used to instantiate an
  LayoutLMv2 model according to the specified arguments, defining the model architecture. Instantiating a
  configuration with the defaults will yield a similar configuration to that of the LayoutLMv2
  [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      vocab_size (`int`, *optional*, defaults to 30522):
          Vocabulary size of the LayoutLMv2 model. Defines the number of different tokens that can be represented by
          the `inputs_ids` passed when calling [`LayoutLMv2Model`] or [`TFLayoutLMv2Model`].
      hidden_size (`int`, *optional*, defaults to 768):
          Dimension of the encoder layers and the pooler layer.
      num_hidden_layers (`int`, *optional*, defaults to 12):
          Number of hidden layers in the Transformer encoder.
      num_attention_heads (`int`, *optional*, defaults to 12):
          Number of attention heads for each attention layer in the Transformer encoder.
      intermediate_size (`int`, *optional*, defaults to 3072):
          Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
      hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"selu"` and `"gelu_new"` are supported.
      hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
      attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout ratio for the attention probabilities.
      max_position_embeddings (`int`, *optional*, defaults to 512):
          The maximum sequence length that this model might ever be used with. Typically set this to something large
          just in case (e.g., 512 or 1024 or 2048).
      type_vocab_size (`int`, *optional*, defaults to 2):
          The vocabulary size of the `token_type_ids` passed when calling [`LayoutLMv2Model`] or
          [`TFLayoutLMv2Model`].
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      layer_norm_eps (`float`, *optional*, defaults to 1e-12):
          The epsilon used by the layer normalization layers.
      max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
          The maximum value that the 2D position embedding might ever be used with. Typically set this to something
          large just in case (e.g., 1024).
      max_rel_pos (`int`, *optional*, defaults to 128):
          The maximum number of relative positions to be used in the self-attention mechanism.
      rel_pos_bins (`int`, *optional*, defaults to 32):
          The number of relative position bins to be used in the self-attention mechanism.
      fast_qkv (`bool`, *optional*, defaults to `True`):
          Whether or not to use a single matrix for the queries, keys, values in the self-attention layers.
      max_rel_2d_pos (`int`, *optional*, defaults to 256):
          The maximum number of relative 2D positions in the self-attention mechanism.
      rel_2d_pos_bins (`int`, *optional*, defaults to 64):
          The number of 2D relative position bins in the self-attention mechanism.
      image_feature_pool_shape (`List[int]`, *optional*, defaults to [7, 7, 256]):
          The shape of the average-pooled feature map.
      coordinate_size (`int`, *optional*, defaults to 128):
          Dimension of the coordinate embeddings.
      shape_size (`int`, *optional*, defaults to 128):
          Dimension of the width and height embeddings.
      has_relative_attention_bias (`bool`, *optional*, defaults to `True`):
          Whether or not to use a relative attention bias in the self-attention mechanism.
      has_spatial_attention_bias (`bool`, *optional*, defaults to `True`):
          Whether or not to use a spatial attention bias in the self-attention mechanism.
      has_visual_segment_embedding (`bool`, *optional*, defaults to `False`):
          Whether or not to add visual segment embeddings.
      detectron2_config_args (`dict`, *optional*):
          Dictionary containing the configuration arguments of the Detectron2 visual backbone. Refer to [this
          file](https://github.com/microsoft/unilm/blob/master/layoutlmft/layoutlmft/models/layoutlmv2/detectron2_config.py)
          for details regarding default values.
  
  Example:
  
  ```python
  >>> from transformers import LayoutLMv2Config, LayoutLMv2Model
  
  >>> # Initializing a LayoutLMv2 microsoft/layoutlmv2-base-uncased style configuration
  >>> configuration = LayoutLMv2Config()
  
  >>> # Initializing a model (with random weights) from the microsoft/layoutlmv2-base-uncased style configuration
  >>> model = LayoutLMv2Model(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def get_default_detectron2_config(self):
      return {
          "MODEL.MASK_ON": True,
          "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
          "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
          "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
          "MODEL.ANCHOR_GENERATOR.SIZES": [
              [32],
              [64],
              [128],
              [256],
              [512],
          ],
          "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
          "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
          "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
          "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
          "MODEL.POST_NMS_TOPK_TEST": 1000,
          "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
          "MODEL.ROI_HEADS.NUM_CLASSES": 5,
          "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
          "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
          "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
          "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
          "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
          "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
          "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
          "MODEL.RESNETS.DEPTH": 101,
          "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
          "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
          "MODEL.RESNETS.OUT_FEATURES": [
              "res2",
              "res3",
              "res4",
              "res5",
          ],
          "MODEL.RESNETS.NUM_GROUPS": 32,
          "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
          "MODEL.RESNETS.STRIDE_IN_1X1": False,
      }
  
imports: from ...configuration_utils import PretrainedConfig
  from ...utils import is_detectron2_available, logging
label_desc: Use image node_id 2 for calling the LayoutLMv2Config obj's underlying member method code with example usage: obj.get_default_detectron2_config() and returns: dict