code: def block_reduction_a(inputs, scope=None, is_train=False):
      """Builds Reduction-A block for Inception v4 network."""
      # By default use stride=1 and SAME padding
  
      with tf.variable_scope(scope, "BlockReductionA", [inputs]):
          with tf.variable_scope("Branch_0"):
              branch_0, _ = conv_module(
                  inputs,
                  n_out_channel=384,
                  filter_size=(3, 3),
                  strides=(2, 2),
                  padding="VALID",
                  batch_norm_init=None,
                  is_train=is_train,
                  use_batchnorm=True,
                  activation_fn="ReLU",
                  name="Conv2d_1a_3x3",
              )
  
          with tf.variable_scope("Branch_1"):
              branch_1, _ = conv_module(
                  inputs,
                  n_out_channel=192,
                  filter_size=(1, 1),
                  strides=(1, 1),
                  padding="SAME",
                  batch_norm_init=None,
                  is_train=is_train,
                  use_batchnorm=True,
                  activation_fn="ReLU",
                  name="Conv2d_0a_1x1",
              )
  
              branch_1, _ = conv_module(
                  branch_1,
                  n_out_channel=224,
                  filter_size=(3, 3),
                  strides=(1, 1),
                  padding="SAME",
                  batch_norm_init=None,
                  is_train=is_train,
                  use_batchnorm=True,
                  activation_fn="ReLU",
                  name="Conv2d_0b_3x3",
              )
  
              branch_1, _ = conv_module(
                  branch_1,
                  n_out_channel=256,
                  filter_size=(3, 3),
                  strides=(2, 2),
                  padding="VALID",
                  batch_norm_init=None,
                  is_train=is_train,
                  use_batchnorm=True,
                  activation_fn="ReLU",
                  name="Conv2d_1a_3x3",
              )
  
          with tf.variable_scope("Branch_2"):
              branch_2 = tl.layers.MaxPool2d(
                  inputs,
                  (3, 3),
                  strides=(2, 2),
                  padding="VALID",
                  name="MaxPool_1a_3x3",
              )
  
          return tl.layers.ConcatLayer(
              [branch_0, branch_1, branch_2],
              concat_dim=3,
              name="concat_layer",
          )
  
file_path: TensorLayer/tests/utils/custom_layers/inception_blocks.py
imports: import tensorflow
  import tensorlayer
  from tests.utils.custom_layers.basic_layers import conv_module
label_desc: Use image node_id 2 for calling a global function with example usage: block_reduction_a(inputs, scope, is_train) and returns: tl