code: def batchify_with_dict(
      data: List[dict],
      ctx: Optional[mx.context.Context] = None,
      dtype: Optional[Type] = np.float32,
      variable_length: bool = False,
      is_right_pad: bool = True,
  ) -> DataBatch:
      return {
          key: stack(
              data=[item[key] for item in data],
              ctx=ctx,
              dtype=dtype,
              variable_length=variable_length,
              is_right_pad=is_right_pad,
          )
          if not isinstance(data[0][key], dict)
          else batchify_with_dict(data=[item[key] for item in data])
          for key in data[0].keys()
      }
  
imports: from functools import partial
  from typing import Callable, Iterator, List, Optional
  from pathlib import Path
  import mxnet
  import numpy
  from gluonts.core.component import Type, validated
  from gluonts.core.serde import load_json
  from gluonts.dataset.common import DataEntry, Dataset
  from gluonts.dataset.loader import DataBatch, InferenceDataLoader
  from gluonts.model.forecast import Forecast
  from gluonts.model.forecast_generator import ForecastGenerator, SampleForecastGenerator
  from gluonts.mx.batchify import stack
  from gluonts.mx.context import get_mxnet_context
  from gluonts.mx.distribution import DistributionOutput, StudentTOutput
  from gluonts.mx.model.deepar import DeepAREstimator
  from gluonts.mx.model.deepar._network import DeepARPredictionNetwork
  from gluonts.mx.model.predictor import RepresentableBlockPredictor
  from gluonts.mx.trainer import Trainer
  from gluonts.mx.util import import_repr_block
  from gluonts.time_feature import TimeFeature
  from gluonts.transform import InstanceSampler, Transformation
  from gluonts.transform.feature import MissingValueImputation
label_desc: Use image node_id 1 for calling a global function with example usage: batchify_with_dict(data, ctx, dtype, variable_length, is_right_pad) and returns: unknown