class_docstr: This is the configuration class to store the configuration of a [`MptAttention`] class. It is used to instantiate
  attention layers according to the specified arguments, defining the layers architecture. Instantiating a
  configuration with the defaults will yield a similar configuration to that of the MPT
  [mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b) architecture. Most of the arguments are kept for backward
  compatibility with previous MPT models that are hosted on the Hub (previously with `trust_remote_code=True`).
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      attn_type (`str`, *optional*, defaults to `"multihead_attention"`):
          type of attention to use. Options: `"multihead_attention"`, `"multiquery_attention"`.
      attn_pdrop (`float`, *optional*, defaults to 0.0):
          The dropout probability for the attention layers.
      attn_impl (`str`, *optional*, defaults to `"torch"`):
          The attention implementation to use. One of `"torch"`, `"flash"`, or `"triton"`.
      clip_qkv (`float`, *optional*):
          If not `None`, clip the queries, keys, and values in the attention layer to this value.
      softmax_scale (`float`, *optional*, defaults to `None`):
          If not `None`, scale the softmax in the attention layer by this value. If `None`, will default to
          `1/sqrt(hidden_size)`.
      prefix_lm (`bool`, *optional*, defaults to `False`)):
          Whether the model should operate as a Prefix LM. This requires passing an extra `prefix_mask` argument
          which indicates which tokens belong to the prefix. Tokens in the prefix can attend to one another
          bi-directionally. Tokens outside the prefix use causal attention.
      qk_ln (`bool`, *optional*, defaults to `False`):
          Whether to apply layer normalization to the queries and keys in the attention layer.
      attn_uses_sequence_id (`bool`, *optional*, defaults to `False`)):
          Whether to restrict attention to tokens that have the same token_type_ids. When the model is in `train`
          mode, this requires passing an extra *token_type_ids* argument which indicates which sub-sequence each
          token belongs to. Defaults to `False` meaning any provided *token_type_ids* will be ignored.
      alibi (`bool`, *optional*, defaults to `True`):
          Whether or not to use the alibi bias instead of positional embedding.
      alibi_bias_max (`int`, *optional*, defaults to 8):
          The maximum value of the alibi bias.
code: def from_pretrained(
      cls, pretrained_model_name_or_path, **kwargs
  ) -> "PretrainedConfig":
      cls._set_token_in_kwargs(kwargs)
  
      config_dict, kwargs = cls.get_config_dict(
          pretrained_model_name_or_path, **kwargs
      )
  
      if config_dict.get("model_type") == "mpt":
          config_dict = config_dict["attn_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/mpt/configuration_mpt.py
imports: from typing import TYPE_CHECKING, Optional, Union
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 2 for calling the MptAttentionConfig obj's underlying member method code with example usage: obj.from_pretrained(cls, pretrained_model_name_or_path) and returns: cls