code: def __init__(
      self,
      device,
      batch_size,
      pos_size_iter,
      num_threads=1,
      device_id=0,
      is_fused_decoder=False,
      axes=None,
      axis_names=None,
      normalized_anchor=True,
      normalized_shape=True,
  ):
      super().__init__(batch_size, num_threads, device_id, seed=1234)
      self.is_fused_decoder = is_fused_decoder
      self.pos_size_iter = pos_size_iter
      self.device = device
      self.input = ops.readers.Caffe(
          path=caffe_db_folder, random_shuffle=False
      )
      self.input_crop_pos = ops.ExternalSource()
      self.input_crop_size = ops.ExternalSource()
  
      if self.is_fused_decoder:
          self.decode = ops.decoders.ImageSlice(
              device="cpu",
              output_type=types.RGB,
              normalized_anchor=normalized_anchor,
              normalized_shape=normalized_shape,
              axis_names=axis_names,
              axes=axes,
          )
      else:
          self.decode = ops.decoders.Image(
              device="cpu", output_type=types.RGB
          )
          self.slice = ops.Slice(
              device=self.device,
              normalized_anchor=normalized_anchor,
              normalized_shape=normalized_shape,
              axis_names=axis_names,
              axes=axes,
          )
  
imports: from nvidia.dali import Pipeline, pipeline_def, ops, fn, types
  import numpy
  import os
  from functools import partial
  from math import floor
  from test_utils import compare_pipelines, get_dali_extra_path, RandomDataIterator, generator_random_axes_for_3d_input, generator_random_data, as_array
  from nose_utils import assert_raises
  from nose2.tools import params
label_desc: Use image node_id 1 to create a new SlicePipeline object from inherited base classes: Pipeline with example: obj = SlicePipeline(device, batch_size, pos_size_iter, num_threads, device_id, is_fused_decoder, axes, axis_names, normalized_anchor, normalized_shape)