code: def _get_recmetric_module():
      rec_task = DefaultTaskInfo
  
      metrics_config = MetricsConfig(
          rec_tasks=[rec_task],
          rec_metrics={
              RecMetricEnum.MSE: RecMetricDef(
                  rec_tasks=[rec_task], window_size=5
              ),
          },
          qps_metric=QPSDef(),
          state_metrics=[],
      )
      return generate_metric_module(
          RecMetricModule,
          metrics_config=metrics_config,
          batch_size=2,
          world_size=1,
          my_rank=1,
          state_metrics_mapping={},
          device="cpu",
      )
  
imports: import copy
  import unittest
  import numpy
  import numpy.testing
  import torch
  from libfb.py.collections import AttrDict
  from pytorch_lightning.loggers import TensorBoardLogger
  from reagent.core.types import CBInput
  from reagent.gym.policies.policy import Policy
  from reagent.gym.policies.samplers.discrete_sampler import GreedyActionSampler
  from reagent.models.linear_regression import LinearRegressionUCB
  from reagent.training.cb.linucb_trainer import LinUCBTrainer
  from reagent.training.cb.utils import add_chosen_arm_features
  from reagent.training.parameters import LinUCBTrainerParameters
  from torchrec.fb.metrics.meta_config import DefaultTaskInfo, MetricsConfig, QPSDef, RecMetricDef, RecMetricEnum
  from torchrec.fb.metrics.meta_metrics import generate_metric_module, RecMetricModule
label_desc: Use image node_id 1 for calling a global function with example usage: _get_recmetric_module() and returns: generate_metric_module