code: def _default_feat_args(dims_or_cardinalities: List[int]):
      if dims_or_cardinalities:
          return dims_or_cardinalities
      return [1]
  
imports: from itertools import chain
  from typing import List, Optional, Dict
  import numpy
  import torch
  from gluonts.core.component import validated
  from gluonts.dataset.field_names import FieldName
  from gluonts.model.forecast_generator import QuantileForecastGenerator
  from gluonts.model.predictor import Predictor
  from gluonts.time_feature import TimeFeature, time_features_from_frequency_str
  from gluonts.torch.model.predictor import PyTorchPredictor
  from gluonts.torch.util import copy_parameters
  from gluonts.transform import Transformation, Chain, ValidationSplitSampler, TestSplitSampler, ExpectedNumInstanceSampler, AddAgeFeature, AsNumpyArray, AddObservedValuesIndicator, AddTimeFeatures, VstackFeatures, SetField
  from pts import Trainer
  from pts.model import PyTorchEstimator
  from pts.model.utils import get_module_forward_input_names
  from .tft_network import TemporalFusionTransformerPredictionNetwork, TemporalFusionTransformerTrainingNetwork
  from .tft_transform import BroadcastTo, TFTInstanceSplitter
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