def block_inception_b(inputs, scope=None, is_train=False):
<EOL>with tf.variable_scope(scope, '<STR_LIT>', [inputs]):<EOL><INDENT>with tf.variable_scope('<STR_LIT>'):<EOL><INDENT>branch_0, _ = conv_module(<EOL>inputs, n_out_channel=<NUM_LIT>, filter_size=(<NUM_LIT:1>, <NUM_LIT:1>), strides=(<NUM_LIT:1>, <NUM_LIT:1>), padding='<STR_LIT>', batch_norm_init=None,<EOL>is_train=is_train, use_batchnorm=True, activation_fn='<STR_LIT>', name='<STR_LIT>'<EOL>)<EOL><DEDENT>with tf.variable_scope('<STR_LIT>'):<EOL><INDENT>branch_1, _ = conv_module(<EOL>inputs, n_out_channel=<NUM_LIT>, filter_size=(<NUM_LIT:1>, <NUM_LIT:1>), strides=(<NUM_LIT:1>, <NUM_LIT:1>), padding='<STR_LIT>', batch_norm_init=None,<EOL>is_train=is_train, use_batchnorm=True, activation_fn='<STR_LIT>', name='<STR_LIT>'<EOL>)<EOL>branch_1, _ = conv_module(<EOL>branch_1, n_out_channel=<NUM_LIT>, filter_size=(<NUM_LIT:1>, <NUM_LIT:7>), strides=(<NUM_LIT:1>, <NUM_LIT:1>), padding='<STR_LIT>', batch_norm_init=None,<EOL>is_train=is_train, use_batchnorm=True, activation_fn='<STR_LIT>', name='<STR_LIT>'<EOL>)<EOL>branch_1, _ = conv_module(<EOL>branch_1, n_out_channel=<NUM_LIT>, filter_size=(<NUM_LIT:7>, <NUM_LIT:1>), strides=(<NUM_LIT:1>, <NUM_LIT:1>), padding='<STR_LIT>', batch_norm_init=None,<EOL>is_train=is_train, use_batchnorm=True, activation_fn='<STR_LIT>', name='<STR_LIT>'<EOL>)<EOL><DEDENT>with tf.variable_scope('<STR_LIT>'):<EOL><INDENT>branch_2, _ = conv_module(<EOL>inputs, n_out_channel=<NUM_LIT>, filter_size=(<NUM_LIT:1>, <NUM_LIT:1>), strides=(<NUM_LIT:1>, <NUM_LIT:1>), padding='<STR_LIT>', batch_norm_init=None,<EOL>is_train=is_train, use_batchnorm=True, activation_fn='<STR_LIT>', name='<STR_LIT>'<EOL>)<EOL>branch_2, _ = conv_module(<EOL>branch_2, n_out_channel=<NUM_LIT>, filter_size=(<NUM_LIT:7>, <NUM_LIT:1>), strides=(<NUM_LIT:1>, <NUM_LIT:1>), padding='<STR_LIT>', batch_norm_init=None,<EOL>is_train=is_train, use_batchnorm=True, activation_fn='<STR_LIT>', name='<STR_LIT>'<EOL>)<EOL>branch_2, _ = conv_module(<EOL>branch_2, n_out_channel=<NUM_LIT>, filter_size=(<NUM_LIT:1>, <NUM_LIT:7>), strides=(<NUM_LIT:1>, <NUM_LIT:1>), padding='<STR_LIT>', batch_norm_init=None,<EOL>is_train=is_train, use_batchnorm=True, activation_fn='<STR_LIT>', name='<STR_LIT>'<EOL>)<EOL>branch_2, _ = conv_module(<EOL>branch_2, n_out_channel=<NUM_LIT>, filter_size=(<NUM_LIT:7>, <NUM_LIT:1>), strides=(<NUM_LIT:1>, <NUM_LIT:1>), padding='<STR_LIT>', batch_norm_init=None,<EOL>is_train=is_train, use_batchnorm=True, activation_fn='<STR_LIT>', name='<STR_LIT>'<EOL>)<EOL>branch_2, _ = conv_module(<EOL>branch_2, n_out_channel=<NUM_LIT>, filter_size=(<NUM_LIT:1>, <NUM_LIT:7>), strides=(<NUM_LIT:1>, <NUM_LIT:1>), padding='<STR_LIT>', batch_norm_init=None,<EOL>is_train=is_train, use_batchnorm=True, activation_fn='<STR_LIT>', name='<STR_LIT>'<EOL>)<EOL><DEDENT>with tf.variable_scope('<STR_LIT>'):<EOL><INDENT>branch_3 = tl.layers.MeanPool2d(<EOL>inputs, filter_size=(<NUM_LIT:3>, <NUM_LIT:3>), strides=(<NUM_LIT:1>, <NUM_LIT:1>), padding='<STR_LIT>', name='<STR_LIT>'<EOL>)<EOL>branch_3, _ = conv_module(<EOL>branch_3, n_out_channel=<NUM_LIT>, filter_size=(<NUM_LIT:1>, <NUM_LIT:1>), strides=(<NUM_LIT:1>, <NUM_LIT:1>), padding='<STR_LIT>', batch_norm_init=None,<EOL>is_train=is_train, use_batchnorm=True, activation_fn='<STR_LIT>', name='<STR_LIT>'<EOL>)<EOL><DEDENT>return tl.layers.ConcatLayer([branch_0, branch_1, branch_2, branch_3], concat_dim=<NUM_LIT:3>, name='<STR_LIT>')<EOL><DEDENT>
Builds Inception-B block for Inception v4 network.