code: def __init__(
      self,
      device,
      batch_size,
      iterator,
      data_shape,
      data_layout,
      dtype,
      num_threads=1,
      device_id=0,
      dictionary={},
      default_value=0.0,
  ):
      super().__init__(batch_size, num_threads, device_id)
      self.device = device
      self.iterator = iterator
      self.inputs = ops.ExternalSource()
      self.data_shape = data_shape
      self.data_layout = data_layout
  
      if dictionary:
          keys = [k for k in dictionary.keys()]
          values = [dictionary[k] for k in keys]
          self.lookup = ops.LookupTable(
              device=self.device,
              dtype=dtype,
              default_value=default_value,
              keys=keys,
              values=values,
          )
      else:
          self.lookup = ops.LookupTable(
              device=self.device,
              dtype=dtype,
              default_value=default_value,
          )
  
imports: import numpy
  import random
  from nvidia.dali import fn, types, ops, pipeline_def
  from nvidia.dali.pipeline import Pipeline
  from test_utils import RandomlyShapedDataIterator
  from test_utils import compare_pipelines
  from nose2.tools import params
label_desc: Use image node_id 1 to create a new LookupTablePipeline object from inherited base classes: Pipeline with example: obj = LookupTablePipeline(device, batch_size, iterator, data_shape, data_layout, dtype, num_threads, device_id, dictionary, default_value)