code: def _default_feat_args(dims_or_cardinalities: List[int]):
      if dims_or_cardinalities:
          return dims_or_cardinalities
      return [1]
  
imports: from functools import partial
  from itertools import chain
  from typing import Dict, List, Optional
  from mxnet.gluon import HybridBlock
  from gluonts.core.component import validated
  from gluonts.dataset.common import Dataset
  from gluonts.dataset.field_names import FieldName
  from gluonts.dataset.loader import DataLoader, TrainDataLoader, ValidationDataLoader
  from gluonts.model.forecast_generator import QuantileForecastGenerator
  from gluonts.mx.batchify import batchify
  from gluonts.mx.model.estimator import GluonEstimator
  from gluonts.mx.model.predictor import RepresentableBlockPredictor
  from gluonts.mx.trainer import Trainer
  from gluonts.mx.util import copy_parameters, get_hybrid_forward_input_names
  from gluonts.time_feature import Constant, TimeFeature, time_features_from_frequency_str
  from gluonts.transform import AddObservedValuesIndicator, AddTimeFeatures, AsNumpyArray, Chain, ExpectedNumInstanceSampler, InstanceSampler, SelectFields, SetField, TestSplitSampler, Transformation, ValidationSplitSampler, VstackFeatures
  from gluonts.transform.split import TFTInstanceSplitter
  from ._network import TemporalFusionTransformerPredictionNetwork, TemporalFusionTransformerTrainingNetwork
  from ._transform import BroadcastTo
label_desc: Use image node_id 1 for calling a global function with example usage: _default_feat_args(dims_or_cardinalities) and returns: list, dims_or_cardinalities