class_docstr: This is the configuration class to store the configuration of a [`ClapAudioModel`]. It is used to instantiate a
  CLAP audio encoder according to the specified arguments, defining the model architecture. Instantiating a
  configuration with the defaults will yield a similar configuration to that of the audio encoder of the CLAP
  [laion/clap-htsat-fused](https://huggingface.co/laion/clap-htsat-fused) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      window_size (`int`, *optional*, defaults to 8):
          Image size of the spectrogram
      num_mel_bins (`int`, *optional*, defaults to 64):
          Number of mel features used per frames. Should correspond to the value used in the `ClapProcessor` class.
      spec_size (`int`, *optional*, defaults to 256):
          Desired input size of the spectrogram that the model supports. It can be different from the output of the
          `ClapFeatureExtractor`, in which case the input features will be resized. Corresponds to the `image_size`
          of the audio models.
      hidden_act (`str`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
          `"relu"`, `"silu"` and `"gelu_new"` are supported.
      patch_size (`int`, *optional*, defaults to 4):
          Patch size for the audio spectrogram
      patch_stride (`list`, *optional*, defaults to `[4, 4]`):
          Patch stride for the audio spectrogram
      num_classes (`int`, *optional*, defaults to 527):
          Number of classes used for the head training
      hidden_size (`int`, *optional*, defaults to 768):
          Hidden size of the output of the audio encoder. Correspond to the dimension of the penultimate layer's
          output,which is sent to the projection MLP layer.
      projection_dim (`int`, *optional*, defaults to 512):
          Hidden size of the projection layer.
      depths (`list`, *optional*, defaults to `[2, 2, 6, 2]`):
          Depths used for the Swin Layers of the audio model
      num_attention_heads (`list`, *optional*, defaults to `[4, 8, 16, 32]`):
          Number of attention heads used for the Swin Layers of the audio model
      enable_fusion (`bool`, *optional*, defaults to `False`):
          Whether or not to enable patch fusion. This is the main contribution of the authors, and should give the
          best results.
      hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
          The dropout probabilitiy for all fully connected layers in the encoder.
      fusion_type (`[type]`, *optional*):
          Fusion type used for the patch fusion.
      patch_embed_input_channels (`int`, *optional*, defaults to 1):
          Number of channels used for the input spectrogram
      flatten_patch_embeds (`bool`, *optional*, defaults to `True`):
          Whether or not to flatten the patch embeddings
      patch_embeds_hidden_size (`int`, *optional*, defaults to 96):
          Hidden size of the patch embeddings. It is used as the number of output channels.
      enable_patch_layer_norm (`bool`, *optional*, defaults to `True`):
          Whether or not to enable layer normalization for the patch embeddings
      drop_path_rate (`float`, *optional*, defaults to 0.0):
          Drop path rate for the patch fusion
      attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
          The dropout ratio for the attention probabilities.
      qkv_bias (`bool`, *optional*, defaults to `True`):
          Whether or not to add a bias to the query, key, value projections.
      mlp_ratio (`float`, *optional*, defaults to 4.0):
          Ratio of the mlp hidden dim to embedding dim.
      aff_block_r (`int`, *optional*, defaults to 4):
          downsize_ratio used in the AudioFF block
      num_hidden_layers (`int`, *optional*, defaults to 4):
          Number of hidden layers in the Transformer encoder.
      projection_hidden_act (`str`, *optional*, defaults to `"relu"`):
          The non-linear activation function (function or string) in the projection layer. If string, `"gelu"`,
          `"relu"`, `"silu"` and `"gelu_new"` are supported.
      layer_norm_eps (`[type]`, *optional*, defaults to 1e-05):
          The epsilon used by the layer normalization layers.
      initializer_factor (`float`, *optional*, defaults to 1.0):
          A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
          testing).
  
  Example:
  
  ```python
  >>> from transformers import ClapAudioConfig, ClapAudioModel
  
  >>> # Initializing a ClapAudioConfig with laion/clap-htsat-fused style configuration
  >>> configuration = ClapAudioConfig()
  
  >>> # Initializing a ClapAudioModel (with random weights) from the laion/clap-htsat-fused style configuration
  >>> model = ClapAudioModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
code: def from_pretrained(
      cls,
      pretrained_model_name_or_path: Union[str, os.PathLike],
      **kwargs,
  ) -> "PretrainedConfig":
      cls._set_token_in_kwargs(kwargs)
  
      config_dict, kwargs = cls.get_config_dict(
          pretrained_model_name_or_path, **kwargs
      )
  
      # get the audio config dict if we are loading from ClapConfig
      if config_dict.get("model_type") == "clap":
          config_dict = config_dict["audio_config"]
  
      if (
          "model_type" in config_dict
          and hasattr(cls, "model_type")
          and config_dict["model_type"] != cls.model_type
      ):
          logger.warning(
              f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
              f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
          )
  
      return cls.from_dict(config_dict, **kwargs)
  
file_path: transformers/src/transformers/models/clap/configuration_clap.py
imports: import os
  from typing import Union
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 2 for calling the ClapAudioConfig obj's underlying member method code with example usage: obj.from_pretrained(cls, pretrained_model_name_or_path) and returns: cls