class_docstr: Initialize the Lightseq Transformer Encoder Layer.
  
  Static variable:
      layer_id: The layer-index counter starting from 0 and incrementing by 1 every time a layer object is instantiated,
      e.g. if a model has 24 transformer layers, layer_id goes from 0 to 23.
  Arguments:
      config: An object of LSTransformerEncoderLayer config, see get_config
  
      initial_weights: Optional: Only used for unit test
  
      initial_biases: Optional: Only used for unit test
code: def from_huggingface(layer, training_args, model_config):
      ls_gpt_config = gen_ls_gpt_enc_config(training_args, model_config)
      init_ws, init_bs = get_hf_gpt_enc_layer_params(
          layer, ls_gpt_config
      )
      return LSHFGptEncoderLayer(ls_gpt_config, init_ws, init_bs).cuda()
  
file_path: lightseq/lightseq/training/ops/pytorch/gpt_layer.py
imports: import torch
  from lightseq.training.ops.pytorch import TransformerBuilder
  from lightseq.training.ops.pytorch.transformer_encoder_layer import LSTransformerEncoderLayer
  from lightseq.training.ops.pytorch.quantization import weight_quant_config, act_quant_config
label_desc: Use image node_id 3 for calling the LSGptEncoderLayer obj's underlying member method code with example usage: obj.from_huggingface(layer, training_args, model_config) and returns: LSHFGptEncoderLayer