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 __init__(self, config, initial_weights=None, initial_biases=None):
      super(LSGptEncoderLayer, self).__init__(
          config,
          initial_weights=initial_weights,
          initial_biases=initial_biases,
      )
  
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 1 to create a new LSGptEncoderLayer object from inherited base classes: LSTransformerEncoderLayer with example: obj = LSGptEncoderLayer(config, initial_weights, initial_biases)