code: def build_encoder_layer(self, cfg):
      layer = XMODTransformerEncoderLayerBase(cfg)
      checkpoint = cfg.checkpoint_activations
      if checkpoint:
          offload_to_cpu = cfg.offload_activations
          layer = checkpoint_wrapper(
              layer, offload_to_cpu=offload_to_cpu
          )
      # if we are checkpointing, enforce that FSDP always wraps the
      # checkpointed layer, regardless of layer size
      min_params_to_wrap = (
          cfg.min_params_to_wrap if not checkpoint else 0
      )
      layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
      return layer
  
imports: from ..roberta.model_xlmr import XLMRModel
  from fairseq.models.xmod.transformer_layer_xmod import XMODTransformerEncoderLayerBase
  from ..roberta.model import base_architecture, RobertaEncoder
  from fairseq.models.transformer import TransformerEncoder
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from typing import Optional
  from fairseq.models.xmod.hub_interface import XMODHubInterface
  import torch
  from fairseq.distributed import fsdp_wrap
  from fairseq.models import register_model, register_model_architecture
  from fairseq.modules.checkpoint_activations import checkpoint_wrapper
label_desc: Use image node_id 1 for calling the XMODTransformerEncoder obj's underlying member method code with example usage: obj.build_encoder_layer(cfg) and returns: layer