code: def forward(
      ctx,
      input,
      input_mask,
      parameters,
      config,
  ):
      cuda_module = layer_cuda_module
      forward_func = (
          cuda_module.transformer_encoder_layer_fw_fp16
          if config.fp16
          else cuda_module.transformer_encoder_layer_fw_fp32
      )
      if config.fp16:
          input = input.to(torch.half)
          input_mask = input_mask.to(torch.half)
  
      (output,) = forward_func(
          config.layer_id, input, input_mask, config.training
      )
  
      if config.is_grad_enabled and config.training:
          ctx.save_for_backward(output, input, input_mask)
          ctx.config = config
      return output
  
imports: import math
  import copy
  import torch
  from torch import nn
  from torch.autograd import Function
  from lightseq.training.ops.pytorch.layer_base import TransformerEncoderLayerBase
  from .None import layer_cuda_module
  from lightseq.training.ops.pytorch.util import copy_para, state_dict, calc_offset
label_desc: Use image node_id 1 for calling the LSTransformerEncoderFuncNew obj's underlying member method code with example usage: obj.forward(ctx, input, input_mask, parameters, config) and returns: output