code: def inception_v1_no_aux_classifier(class_num, has_dropout=True):
      model = Sequential()
      model.add(
          SpatialConvolution(3, 64, 7, 7, 2, 2, 3, 3, 1, False)
          .set_init_method(
              weight_init_method=Xavier(),
              bias_init_method=ConstInitMethod(0.1),
          )
          .set_name("conv1/7x7_s2")
      )
      model.add(ReLU(True).set_name("conv1/relu_7x7"))
      model.add(
          SpatialMaxPooling(3, 3, 2, 2, to_ceil=True).set_name(
              "pool1/3x3_s2"
          )
      )
      model.add(
          SpatialCrossMapLRN(5, 0.0001, 0.75).set_name("pool1/norm1")
      )
      model.add(
          SpatialConvolution(64, 64, 1, 1, 1, 1)
          .set_init_method(
              weight_init_method=Xavier(),
              bias_init_method=ConstInitMethod(0.1),
          )
          .set_name("conv2/3x3_reduce")
      )
      model.add(ReLU(True).set_name("conv2/relu_3x3_reduce"))
      model.add(
          SpatialConvolution(64, 192, 3, 3, 1, 1, 1, 1)
          .set_init_method(
              weight_init_method=Xavier(),
              bias_init_method=ConstInitMethod(0.1),
          )
          .set_name("conv2/3x3")
      )
      model.add(ReLU(True).set_name("conv2/relu_3x3"))
      model.add(
          SpatialCrossMapLRN(5, 0.0001, 0.75).set_name("conv2/norm2")
      )
      model.add(
          SpatialMaxPooling(3, 3, 2, 2, to_ceil=True).set_name(
              "pool2/3x3_s2"
          )
      )
      model.add(
          inception_layer_v1(
              192,
              t([t([64]), t([96, 128]), t([16, 32]), t([32])]),
              "inception_3a/",
          )
      )
      model.add(
          inception_layer_v1(
              256,
              t([t([128]), t([128, 192]), t([32, 96]), t([64])]),
              "inception_3b/",
          )
      )
      model.add(SpatialMaxPooling(3, 3, 2, 2, to_ceil=True))
      model.add(
          inception_layer_v1(
              480,
              t([t([192]), t([96, 208]), t([16, 48]), t([64])]),
              "inception_4a/",
          )
      )
      model.add(
          inception_layer_v1(
              512,
              t([t([160]), t([112, 224]), t([24, 64]), t([64])]),
              "inception_4b/",
          )
      )
      model.add(
          inception_layer_v1(
              512,
              t([t([128]), t([128, 256]), t([24, 64]), t([64])]),
              "inception_4c/",
          )
      )
      model.add(
          inception_layer_v1(
              512,
              t([t([112]), t([144, 288]), t([32, 64]), t([64])]),
              "inception_4d/",
          )
      )
      model.add(
          inception_layer_v1(
              528,
              t([t([256]), t([160, 320]), t([32, 128]), t([128])]),
              "inception_4e/",
          )
      )
      model.add(SpatialMaxPooling(3, 3, 2, 2, to_ceil=True))
      model.add(
          inception_layer_v1(
              832,
              t([t([256]), t([160, 320]), t([32, 128]), t([128])]),
              "inception_5a/",
          )
      )
      model.add(
          inception_layer_v1(
              832,
              t([t([384]), t([192, 384]), t([48, 128]), t([128])]),
              "inception_5b/",
          )
      )
      model.add(
          SpatialAveragePooling(7, 7, 1, 1).set_name("pool5/7x7_s1")
      )
      if has_dropout:
          model.add(Dropout(0.4).set_name("pool5/drop_7x7_s1"))
      model.add(View([1024], num_input_dims=3))
      model.add(
          Linear(1024, class_num)
          .set_init_method(
              weight_init_method=Xavier(), bias_init_method=Zeros()
          )
          .set_name("loss3/classifier")
      )
      model.add(LogSoftMax().set_name("loss3/loss3"))
      model.reset()
      return model
  
file_path: analytics-zoo/pyzoo/zoo/examples/inception/inception.py
imports: from optparse import OptionParser
  from math import ceil
  from datetime import datetime
  from zoo.common.nncontext import *
  from zoo.feature.image import *
  from zoo.pipeline.nnframes import *
  from zoo.pipeline.estimator import *
  from bigdl.optim.optimizer import *
  from bigdl.util.common import *
  from bigdl.nn.layer import *
  from bigdl.nn.criterion import *
  from bigdl.nn.initialization_method import *
label_desc: Use image node_id 5 for calling a global function with example usage: inception_v1_no_aux_classifier(class_num, has_dropout) and returns: model