class_docstr: An implementation of the "Simplicial Embeddings"_, as proposed by Lavoie et. al
  
  Arguments:
      - L: the number of embedding chunks
      - temperature: optional scaling parameter for the softmax operation.
          A small (<1.) temperature will lead to a sparse representation (up to one-hot),
          while a large (>1.) temperature will make the vector more uniform
  
  _"Simplicial Embeddings": https://arxiv.org/pdf/2204.00616.pdf
code: def from_config(
      cls: Type[Self], config: SimplicialEmbeddingConfig
  ) -> Self:
      # Generate the class inputs from the config
      fields = asdict(config)
  
      return cls(**fields)
  
file_path: xformers/xformers/components/simplicial_embedding.py
imports: from dataclasses import asdict, dataclass
  from typing import Optional, Type, TypeVar
  import torch
  from xformers import _is_triton_available
label_desc: Use image node_id 3 for calling the SimplicialEmbedding obj's underlying member method code with example usage: obj.from_config(cls, config) and returns: cls