text: """
      InceptionV4 for ImageNet-1K, implemented in TensorFlow.
      Original paper: 'Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning,'
      https://arxiv.org/abs/1602.07261.
  """
  
  __all__ = ['InceptionV4', 'inceptionv4']
  
  import os
  import tensorflow as tf
  import tensorflow.keras.layers as nn
  from models.common import MaxPool2d, AvgPool2d, Conv2d, BatchNorm, SimpleSequential, Concurrent, flatten, is_channels_first,\
      get_channel_axis
  
  
  class InceptConv(nn.Layer):
      """
      InceptionV4 specific convolution block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      kernel_size : int or tuple/list of 2 int
          Convolution window size.
      strides : int or tuple/list of 2 int
          Strides of the convolution.
      padding : int or tuple/list of 2 int
          Padding value for convolution layer.
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   kernel_size,
                   strides,
                   padding,
                   data_format="channels_last",
                   **kwargs):
          super(InceptConv, self).__init__(**kwargs)
          self.conv = Conv2d(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=kernel_size,
              strides=strides,
              padding=padding,
              use_bias=False,
              data_format=data_format,
              name="conv")
          self.bn = BatchNorm(
              momentum=0.1,
              epsilon=1e-3,
              data_format=data_format,
              name="bn")
          self.activ = nn.ReLU()
  
      def call(self, x, training=None):
          x = self.conv(x)
          x = self.bn(x, training=training)
          x = self.activ(x)
          return x
  
  
  def incept_conv1x1(in_channels,
                     out_channels,
                     data_format="channels_last",
                     **kwargs):
      """
      1x1 version of the InceptionV4 specific convolution block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      return InceptConv(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=1,
          strides=1,
          padding=0,
          data_format=data_format,
          **kwargs)
  
  
  def incept_conv3x3(in_channels,
                     out_channels,
                     strides,
                     padding=1,
                     data_format="channels_last",
                     **kwargs):
      """
      3x3 version of the InceptionV4 specific convolution block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      strides : int or tuple/list of 2 int
          Strides of the convolution.
      padding : int or tuple/list of 2 int, default 0
          Padding value for convolution layer.
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      return InceptConv(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=3,
          strides=strides,
          padding=padding,
          data_format=data_format,
          **kwargs)
  
  
  class MaxPoolBranch(nn.Layer):
      """
      InceptionV4 specific max pooling branch block.
  
      Parameters:
      ----------
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   data_format="channels_last",
                   **kwargs):
          super(MaxPoolBranch, self).__init__(**kwargs)
          self.pool = MaxPool2d(
              pool_size=3,
              strides=2,
              padding=0,
              data_format=data_format,
              name="pool")
  
      def call(self, x, training=None):
          x = self.pool(x)
          return x
  
  
  class AvgPoolBranch(nn.Layer):
      """
      InceptionV4 specific average pooling branch block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   data_format="channels_last",
                   **kwargs):
          super(AvgPoolBranch, self).__init__(**kwargs)
          self.pool = AvgPool2d(
              pool_size=3,
              strides=1,
              padding=1,
              # count_include_pad=False,
              data_format=data_format,
              name="pool")
          self.conv = incept_conv1x1(
              in_channels=in_channels,
              out_channels=out_channels,
              data_format=data_format,
              name="conv")
  
      def call(self, x, training=None):
          x = self.pool(x)
          x = self.conv(x, training=training)
          return x
  
  
  class Conv1x1Branch(nn.Layer):
      """
      InceptionV4 specific convolutional 1x1 branch block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   data_format="channels_last",
                   **kwargs):
          super(Conv1x1Branch, self).__init__(**kwargs)
          self.conv = incept_conv1x1(
              in_channels=in_channels,
              out_channels=out_channels,
              data_format=data_format,
              name="conv")
  
      def call(self, x, training=None):
          x = self.conv(x, training=training)
          return x
  
  
  class Conv3x3Branch(nn.Layer):
      """
      InceptionV4 specific convolutional 3x3 branch block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   data_format="channels_last",
                   **kwargs):
          super(Conv3x3Branch, self).__init__(**kwargs)
          self.conv = incept_conv3x3(
              in_channels=in_channels,
              out_channels=out_channels,
              strides=2,
              padding=0,
              data_format=data_format,
              name="conv")
  
      def call(self, x, training=None):
          x = self.conv(x, training=training)
          return x
  
  
  class ConvSeqBranch(nn.Layer):
      """
      InceptionV4 specific convolutional sequence branch block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels_list : list of tuple of int
          List of numbers of output channels.
      kernel_size_list : list of tuple of int or tuple of tuple/list of 2 int
          List of convolution window sizes.
      strides_list : list of tuple of int or tuple of tuple/list of 2 int
          List of strides of the convolution.
      padding_list : list of tuple of int or tuple of tuple/list of 2 int
          List of padding values for convolution layers.
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   in_channels,
                   out_channels_list,
                   kernel_size_list,
                   strides_list,
                   padding_list,
                   data_format="channels_last",
                   **kwargs):
          super(ConvSeqBranch, self).__init__(**kwargs)
          assert (len(out_channels_list) == len(kernel_size_list))
          assert (len(out_channels_list) == len(strides_list))
          assert (len(out_channels_list) == len(padding_list))
  
          self.conv_list = SimpleSequential(name="conv_list")
          for i, (out_channels, kernel_size, strides, padding) in enumerate(zip(
                  out_channels_list, kernel_size_list, strides_list, padding_list)):
              self.conv_list.children.append(InceptConv(
                  in_channels=in_channels,
                  out_channels=out_channels,
                  kernel_size=kernel_size,
                  strides=strides,
                  padding=padding,
                  data_format=data_format,
                  name="conv{}".format(i + 1)))
              in_channels = out_channels
  
      def call(self, x, training=None):
          x = self.conv_list(x, training=training)
          return x
  
  
  class ConvSeq3x3Branch(nn.Layer):
      """
      InceptionV4 specific convolutional sequence branch block with splitting by 3x3.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      out_channels : int
          Number of output channels.
      mid_channels_list : list of tuple of int
          List of numbers of output channels for middle layers.
      kernel_size_list : list of tuple of int or tuple of tuple/list of 2 int
          List of convolution window sizes.
      strides_list : list of tuple of int or tuple of tuple/list of 2 int
          List of strides of the convolution.
      padding_list : list of tuple of int or tuple of tuple/list of 2 int
          List of padding values for convolution layers.
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   in_channels,
                   out_channels,
                   mid_channels_list,
                   kernel_size_list,
                   strides_list,
                   padding_list,
                   data_format="channels_last",
                   **kwargs):
          super(ConvSeq3x3Branch, self).__init__(**kwargs)
          self.data_format = data_format
  
          self.conv_list = SimpleSequential(name="conv_list")
          for i, (mid_channels, kernel_size, strides, padding) in enumerate(zip(
                  mid_channels_list, kernel_size_list, strides_list, padding_list)):
              self.conv_list.children.append(InceptConv(
                  in_channels=in_channels,
                  out_channels=mid_channels,
                  kernel_size=kernel_size,
                  strides=strides,
                  padding=padding,
                  data_format=data_format,
                  name="conv{}".format(i + 1)))
              in_channels = mid_channels
          self.conv1x3 = InceptConv(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=(1, 3),
              strides=1,
              padding=(0, 1),
              data_format=data_format,
              name="conv1x3")
          self.conv3x1 = InceptConv(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=(3, 1),
              strides=1,
              padding=(1, 0),
              data_format=data_format,
              name="conv3x1")
  
      def call(self, x, training=None):
          x = self.conv_list(x, training=training)
          y1 = self.conv1x3(x, training=training)
          y2 = self.conv3x1(x, training=training)
          x = tf.concat([y1, y2], axis=get_channel_axis(self.data_format))
          return x
  
  
  class InceptionAUnit(nn.Layer):
      """
      InceptionV4 type Inception-A unit.
  
      Parameters:
      ----------
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   data_format="channels_last",
                   **kwargs):
          super(InceptionAUnit, self).__init__(**kwargs)
          in_channels = 384
  
          self.branches = Concurrent(
              data_format=data_format,
              name="branches")
          self.branches.children.append(Conv1x1Branch(
              in_channels=in_channels,
              out_channels=96,
              data_format=data_format,
              name="branch1"))
          self.branches.children.append(ConvSeqBranch(
              in_channels=in_channels,
              out_channels_list=(64, 96),
              kernel_size_list=(1, 3),
              strides_list=(1, 1),
              padding_list=(0, 1),
              data_format=data_format,
              name="branch2"))
          self.branches.children.append(ConvSeqBranch(
              in_channels=in_channels,
              out_channels_list=(64, 96, 96),
              kernel_size_list=(1, 3, 3),
              strides_list=(1, 1, 1),
              padding_list=(0, 1, 1),
              data_format=data_format,
              name="branch3"))
          self.branches.children.append(AvgPoolBranch(
              in_channels=in_channels,
              out_channels=96,
              data_format=data_format,
              name="branch4"))
  
      def call(self, x, training=None):
          x = self.branches(x, training=training)
          return x
  
  
  class ReductionAUnit(nn.Layer):
      """
      InceptionV4 type Reduction-A unit.
  
      Parameters:
      ----------
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   data_format="channels_last",
                   **kwargs):
          super(ReductionAUnit, self).__init__(**kwargs)
          in_channels = 384
  
          self.branches = Concurrent(
              data_format=data_format,
              name="branches")
          self.branches.children.append(ConvSeqBranch(
              in_channels=in_channels,
              out_channels_list=(384,),
              kernel_size_list=(3,),
              strides_list=(2,),
              padding_list=(0,),
              data_format=data_format,
              name="branch1"))
          self.branches.children.append(ConvSeqBranch(
              in_channels=in_channels,
              out_channels_list=(192, 224, 256),
              kernel_size_list=(1, 3, 3),
              strides_list=(1, 1, 2),
              padding_list=(0, 1, 0),
              data_format=data_format,
              name="branch2"))
          self.branches.children.append(MaxPoolBranch(
              data_format=data_format,
              name="branch3"))
  
      def call(self, x, training=None):
          x = self.branches(x, training=training)
          return x
  
  
  class InceptionBUnit(nn.Layer):
      """
      InceptionV4 type Inception-B unit.
  
      Parameters:
      ----------
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   data_format="channels_last",
                   **kwargs):
          super(InceptionBUnit, self).__init__(**kwargs)
          in_channels = 1024
  
          self.branches = Concurrent(
              data_format=data_format,
              name="branches")
          self.branches.children.append(Conv1x1Branch(
              in_channels=in_channels,
              out_channels=384,
              data_format=data_format,
              name="branch1"))
          self.branches.children.append(ConvSeqBranch(
              in_channels=in_channels,
              out_channels_list=(192, 224, 256),
              kernel_size_list=(1, (1, 7), (7, 1)),
              strides_list=(1, 1, 1),
              padding_list=(0, (0, 3), (3, 0)),
              data_format=data_format,
              name="branch2"))
          self.branches.children.append(ConvSeqBranch(
              in_channels=in_channels,
              out_channels_list=(192, 192, 224, 224, 256),
              kernel_size_list=(1, (7, 1), (1, 7), (7, 1), (1, 7)),
              strides_list=(1, 1, 1, 1, 1),
              padding_list=(0, (3, 0), (0, 3), (3, 0), (0, 3)),
              data_format=data_format,
              name="branch3"))
          self.branches.children.append(AvgPoolBranch(
              in_channels=in_channels,
              out_channels=128,
              data_format=data_format,
              name="branch4"))
  
      def call(self, x, training=None):
          x = self.branches(x, training=training)
          return x
  
  
  class ReductionBUnit(nn.Layer):
      """
      InceptionV4 type Reduction-B unit.
  
      Parameters:
      ----------
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   data_format="channels_last",
                   **kwargs):
          super(ReductionBUnit, self).__init__(**kwargs)
          in_channels = 1024
  
          self.branches = Concurrent(
              data_format=data_format,
              name="branches")
          self.branches.children.append(ConvSeqBranch(
              in_channels=in_channels,
              out_channels_list=(192, 192),
              kernel_size_list=(1, 3),
              strides_list=(1, 2),
              padding_list=(0, 0),
              data_format=data_format,
              name="branch1"))
          self.branches.children.append(ConvSeqBranch(
              in_channels=in_channels,
              out_channels_list=(256, 256, 320, 320),
              kernel_size_list=(1, (1, 7), (7, 1), 3),
              strides_list=(1, 1, 1, 2),
              padding_list=(0, (0, 3), (3, 0), 0),
              data_format=data_format,
              name="branch2"))
          self.branches.children.append(MaxPoolBranch(
              data_format=data_format,
              name="branch3"))
  
      def call(self, x, training=None):
          x = self.branches(x, training=training)
          return x
  
  
  class InceptionCUnit(nn.Layer):
      """
      InceptionV4 type Inception-C unit.
  
      Parameters:
      ----------
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   data_format="channels_last",
                   **kwargs):
          super(InceptionCUnit, self).__init__(**kwargs)
          in_channels = 1536
  
          self.branches = Concurrent(
              data_format=data_format,
              name="branches")
          self.branches.children.append(Conv1x1Branch(
              in_channels=in_channels,
              out_channels=256,
              data_format=data_format,
              name="branch1"))
          self.branches.children.append(ConvSeq3x3Branch(
              in_channels=in_channels,
              out_channels=256,
              mid_channels_list=(384,),
              kernel_size_list=(1,),
              strides_list=(1,),
              padding_list=(0,),
              data_format=data_format,
              name="branch2"))
          self.branches.children.append(ConvSeq3x3Branch(
              in_channels=in_channels,
              out_channels=256,
              mid_channels_list=(384, 448, 512),
              kernel_size_list=(1, (3, 1), (1, 3)),
              strides_list=(1, 1, 1),
              padding_list=(0, (1, 0), (0, 1)),
              data_format=data_format,
              name="branch3"))
          self.branches.children.append(AvgPoolBranch(
              in_channels=in_channels,
              out_channels=256,
              data_format=data_format,
              name="branch4"))
  
      def call(self, x, training=None):
          x = self.branches(x, training=training)
          return x
  
  
  class InceptBlock3a(nn.Layer):
      """
      InceptionV4 type Mixed-3a block.
  
      Parameters:
      ----------
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   data_format="channels_last",
                   **kwargs):
          super(InceptBlock3a, self).__init__(**kwargs)
          self.branches = Concurrent(
              data_format=data_format,
              name="branches")
          self.branches.children.append(MaxPoolBranch(
              data_format=data_format,
              name="branch1"))
          self.branches.children.append(Conv3x3Branch(
              in_channels=64,
              out_channels=96,
              data_format=data_format,
              name="branch2"))
  
      def call(self, x, training=None):
          x = self.branches(x, training=training)
          return x
  
  
  class InceptBlock4a(nn.Layer):
      """
      InceptionV4 type Mixed-4a block.
  
      Parameters:
      ----------
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   data_format="channels_last",
                   **kwargs):
          super(InceptBlock4a, self).__init__(**kwargs)
          self.branches = Concurrent(
              data_format=data_format,
              name="branches")
          self.branches.children.append(ConvSeqBranch(
              in_channels=160,
              out_channels_list=(64, 96),
              kernel_size_list=(1, 3),
              strides_list=(1, 1),
              padding_list=(0, 0),
              data_format=data_format,
              name="branch1"))
          self.branches.children.append(ConvSeqBranch(
              in_channels=160,
              out_channels_list=(64, 64, 64, 96),
              kernel_size_list=(1, (1, 7), (7, 1), 3),
              strides_list=(1, 1, 1, 1),
              padding_list=(0, (0, 3), (3, 0), 0),
              data_format=data_format,
              name="branch2"))
  
      def call(self, x, training=None):
          x = self.branches(x, training=training)
          return x
  
  
  class InceptBlock5a(nn.Layer):
      """
      InceptionV4 type Mixed-5a block.
  
      Parameters:
      ----------
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   data_format="channels_last",
                   **kwargs):
          super(InceptBlock5a, self).__init__(**kwargs)
          self.branches = Concurrent(
              data_format=data_format,
              name="branches")
          self.branches.children.append(Conv3x3Branch(
              in_channels=192,
              out_channels=192,
              data_format=data_format,
              name="branch1"))
          self.branches.children.append(MaxPoolBranch(
              data_format=data_format,
              name="branch2"))
  
      def call(self, x, training=None):
          x = self.branches(x, training=training)
          return x
  
  
  class InceptInitBlock(nn.Layer):
      """
      InceptionV4 specific initial block.
  
      Parameters:
      ----------
      in_channels : int
          Number of input channels.
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   in_channels,
                   data_format="channels_last",
                   **kwargs):
          super(InceptInitBlock, self).__init__(**kwargs)
          self.conv1 = InceptConv(
              in_channels=in_channels,
              out_channels=32,
              kernel_size=3,
              strides=2,
              padding=0,
              data_format=data_format,
              name="conv1")
          self.conv2 = InceptConv(
              in_channels=32,
              out_channels=32,
              kernel_size=3,
              strides=1,
              padding=0,
              data_format=data_format,
              name="conv2")
          self.conv3 = InceptConv(
              in_channels=32,
              out_channels=64,
              kernel_size=3,
              strides=1,
              padding=1,
              data_format=data_format,
              name="conv3")
          self.block1 = InceptBlock3a(
              data_format=data_format,
              name="block1")
          self.block2 = InceptBlock4a(
              data_format=data_format,
              name="block2")
          self.block3 = InceptBlock5a(
              data_format=data_format,
              name="block3")
  
      def call(self, x, training=None):
          x = self.conv1(x, training=training)
          x = self.conv2(x, training=training)
          x = self.conv3(x, training=training)
          x = self.block1(x, training=training)
          x = self.block2(x, training=training)
          x = self.block3(x, training=training)
          return x
  
  
  class InceptionV4(tf.keras.Model):
      """
      InceptionV4 model from 'Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning,'
      https://arxiv.org/abs/1602.07261.
  
      Parameters:
      ----------
      dropout_rate : float, default 0.0
          Fraction of the input units to drop. Must be a number between 0 and 1.
      in_channels : int, default 3
          Number of input channels.
      in_size : tuple of two ints, default (299, 299)
          Spatial size of the expected input image.
      classes : int, default 1000
          Number of classification classes.
      data_format : str, default 'channels_last'
          The ordering of the dimensions in tensors.
      """
      def __init__(self,
                   dropout_rate=0.0,
                   in_channels=3,
                   in_size=(299, 299),
                   classes=1000,
                   data_format="channels_last",
                   **kwargs):
          super(InceptionV4, self).__init__(**kwargs)
          self.in_size = in_size
          self.classes = classes
          self.data_format = data_format
          layers = [4, 8, 4]
          normal_units = [InceptionAUnit, InceptionBUnit, InceptionCUnit]
          reduction_units = [ReductionAUnit, ReductionBUnit]
  
          self.features = tf.keras.Sequential(name="features")
          self.features.add(InceptInitBlock(
              in_channels=in_channels,
              data_format=data_format,
              name="init_block"))
  
          for i, layers_per_stage in enumerate(layers):
              stage = tf.keras.Sequential(name="stage{}".format(i + 1))
              for j in range(layers_per_stage):
                  if (j == 0) and (i != 0):
                      unit = reduction_units[i - 1]
                  else:
                      unit = normal_units[i]
                  stage.add(unit(
                      data_format=data_format,
                      name="unit{}".format(j + 1)))
              self.features.add(stage)
          self.features.add(nn.AveragePooling2D(
              pool_size=8,
              strides=1,
              data_format=data_format,
              name="final_pool"))
  
          self.output1 = tf.keras.Sequential(name="output1")
          if dropout_rate > 0.0:
              self.output1.add(nn.Dropout(
                  rate=dropout_rate,
                  name="output1/dropout"))
          self.output1.add(nn.Dense(
              units=classes,
              input_dim=1536,
              name="output1/fc"))
  
      def call(self, x, training=None):
          x = self.features(x, training=training)
          x = flatten(x, self.data_format)
          x = self.output1(x)
          return x
  
  
  def get_inceptionv4(model_name=None,
                      pretrained=False,
                      root=os.path.join("~", ".tensorflow", "models"),
                      **kwargs):
      """
      Create InceptionV4 model with specific parameters.
  
      Parameters:
      ----------
      model_name : str or None, default None
          Model name for loading pretrained model.
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.tensorflow/models'
          Location for keeping the model parameters.
      """
  
      net = InceptionV4(**kwargs)
  
      if pretrained:
          if (model_name is None) or (not model_name):
              raise ValueError("Parameter `model_name` should be properly initialized for loading pretrained model.")
          from .model_store import get_model_file
          in_channels = kwargs["in_channels"] if ("in_channels" in kwargs) else 3
          input_shape = (1,) + (in_channels,) + net.in_size if net.data_format == "channels_first" else\
              (1,) + net.in_size + (in_channels,)
          net.build(input_shape=input_shape)
          net.load_weights(
              filepath=get_model_file(
                  model_name=model_name,
                  local_model_store_dir_path=root))
  
      return net
  
  
  def inceptionv4(**kwargs):
      """
      InceptionV4 model from 'Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning,'
      https://arxiv.org/abs/1602.07261.
  
      Parameters:
      ----------
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      root : str, default '~/.tensorflow/models'
          Location for keeping the model parameters.
      """
      return get_inceptionv4(model_name="inceptionv4", **kwargs)
  
  
  def _test():
      import numpy as np
      import tensorflow.keras.backend as K
  
      data_format = "channels_last"
      pretrained = False
  
      models = [
          inceptionv4,
      ]
  
      for model in models:
  
          net = model(pretrained=pretrained, data_format=data_format)
  
          batch_saze = 14
          x = tf.random.normal((batch_saze, 3, 299, 299) if is_channels_first(data_format) else (batch_saze, 299, 299, 3))
          y = net(x)
          assert (tuple(y.shape.as_list()) == (batch_saze, 1000))
  
          weight_count = sum([np.prod(K.get_value(w).shape) for w in net.trainable_weights])
          print("m={}, {}".format(model.__name__, weight_count))
          assert (model != inceptionv4 or weight_count == 42679816)
  
  
  if __name__ == "__main__":
      _test()
  