def METHOD_NAME(data, growth_rate, bn_size, index):
    """Single densenet layer."""
    bn1 = layers.batch_norm_infer(data, name=f"batch_1_{index}")
    relu1 = relay.nn.relu(bn1)
    conv1 = layers.conv2d(
        relu1, channels=bn_size * growth_rate, kernel_size=(1, 1), name=f"conv2d_1_{index}"
    )
    bn2 = layers.batch_norm_infer(conv1, name="batch_2_" + index)
    relu2 = relay.nn.relu(bn2)
    conv2 = layers.conv2d(
        relu2, channels=growth_rate, kernel_size=(3, 3), padding=(1, 1), name=f"conv2d_2_{index}"
    )
    return conv2