class_docstr: This is the configuration class to store the configuration of a [`TvpModel`]. It is used to instantiate an Tvp
  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 Tvp
  [Intel/tvp-base](https://huggingface.co/Intel/tvp-base) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  
  Args:
      backbone_config (`PretrainedConfig` or `dict`, *optional*):
          The configuration of the backbone model.
      distance_loss_weight (`float`, *optional*, defaults to 1.0):
          The weight of distance loss.
      duration_loss_weight (`float`, *optional*, defaults to 0.1):
          The weight of duration loss.
      visual_prompter_type (`str`, *optional*, defaults to `"framepad"`):
          Visual prompt type. The type of padding. Framepad means padding on each frame. Should be one of "framepad"
          or "framedownpad"
      visual_prompter_apply (`str`, *optional*, defaults to `"replace"`):
          The way of applying visual prompt. Replace means use the value of prompt to change the original value in
          visual inputs. Should be one of "replace", or "add", or "remove".
      visual_prompt_size (`int`, *optional*, defaults to 96):
          The size of visual prompt.
      max_img_size (`int`, *optional*, defaults to 448):
          The maximum size of frame.
      num_frames (`int`, *optional*, defaults to 48):
          The number of frames extracted from a video.
      vocab_size (`int`, *optional*, defaults to 30522):
          Vocabulary size of the Tvp text model. Defines the number of different tokens that can be represented by
          the `inputs_ids` passed when calling [`TvpModel`].
      hidden_size (`int`, *optional*, defaults to 768):
          Dimensionality of the encoder layers.
      intermediate_size (`int`, *optional*, defaults to 3072):
          Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
      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.
      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).
      max_grid_col_position_embeddings (`int`, *optional*, defaults to 100):
          The largest number of horizontal patches from a video frame.
      max_grid_row_position_embeddings (`int`, *optional*, defaults to 100):
          The largest number of vertical patches from a video frame.
      hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout probability of hidden layers.
      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"` ``"quick_gelu"` are supported.
      layer_norm_eps (`float`, *optional*, defaults to 1e-12):
          The epsilon used by the layer normalization layers.
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout probability of attention layers.
docstr: Instantiate a [`TvpConfig`] (or a derived class) from a pre-trained backbone model configuration.
  
  Args:
      backbone_config ([`PretrainedConfig`]):
          The backbone configuration.
  Returns:
      [`TvpConfig`]: An instance of a configuration object
code: def from_backbone_config(
      cls, backbone_config: PretrainedConfig, **kwargs
  ):
      """Instantiate a [`TvpConfig`] (or a derived class) from a pre-trained backbone model configuration.
  
      Args:
          backbone_config ([`PretrainedConfig`]):
              The backbone configuration.
      Returns:
          [`TvpConfig`]: An instance of a configuration object
      """
      return cls(backbone_config=backbone_config, **kwargs)
  
file_path: transformers/src/transformers/models/tvp/configuration_tvp.py
imports: import copy
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto import CONFIG_MAPPING
label_desc: Use image node_id 2 for calling the TvpConfig obj's underlying member method code with example usage: obj.from_backbone_config(cls, backbone_config) and returns: cls