def METHOD_NAME(mxnet_training, py3_only):
    """
    Function to create a pod using kubectl and given container image, and run
    DGL training with MXNet backend
    Args:
        :param mxnet_training: the ECR URI
    """
    training_result = False
    rand_int = random.randint(4001, 6000)
    yaml_path = os.path.join(os.sep, "tmp", f"mxnet_single_node_training_gluonnlp_{rand_int}.yaml")
    pod_name = f"mxnet-single-node-training-gluonnlp-{rand_int}"
    args = (
        "git clone -b master https://github.com/dmlc/gluon-nlp.git && "
        "cd gluon-nlp && git checkout v0.9.0 &&"
        "cd ./scripts/sentiment_analysis/ &&"
        "python sentiment_analysis_cnn.py --batch_size 50 --epochs 20 --dropout 0.5 "
        "--model_mode multichannel --data_name TREC"
    )
    # TODO: Change hardcoded value to read a mapping from the EKS cluster instance.
    cpu_limit = 72
    cpu_limit = str(int(cpu_limit) / 2)
    if "gpu" in mxnet_training:
        args = args + " --gpu 0"
    search_replace_dict = {
        "<POD_NAME>": pod_name,
        "<CONTAINER_NAME>": mxnet_training,
        "<ARGS>": args,
        "<CPU_LIMIT>": cpu_limit,
    }
    eks_utils.write_eks_yaml_file_from_template(
        eks_utils.SINGLE_NODE_TRAINING_TEMPLATE_PATH, yaml_path, search_replace_dict
    )
    try:
        run("kubectl create -f {}".format(yaml_path))
        if eks_utils.is_eks_training_complete(pod_name):
            gluonnlp_out = run("kubectl logs {}".format(pod_name)).stdout
            results = re.search(r"test acc ((?:\d*\.\d+)|\d+)", gluonnlp_out)
            if results is not None:
                accuracy = float(results.groups()[0])
                if accuracy >= 0.75:
                    eks_utils.LOGGER.info(
                        "GluonNLP EKS test succeeded with accuracy {} >= 0.75".format(accuracy)
                    )
                    training_result = True
                else:
                    eks_utils.LOGGER.info(
                        "GluonNLP EKS test FAILED with accuracy {} < 0.75".format(accuracy)
                    )
                    eks_utils.LOGGER.debug(gluonnlp_out)
        assert training_result, f"Training failed"
    finally:
        run("kubectl delete pods {}".format(pod_name))