The residual blocks contain two 2D convolutional layers, each followed by a batch normalization layer and an activation function.

1. Let's define the residual blocks. This code describes the residual blocks completely:

def residual_block(input):
    """
    Residual block in the generator network
    :return:
    """
    x = Conv2D(128 * 4, kernel_size=(3, 3), padding='same', strides=1)(input)
    x = BatchNormalization()(x)
    x = ReLU()(x)

    x = Conv2D(128 * 4, kernel_size=(3, 3), strides=1, padding='same')(x)
    x = BatchNormalization()(x)

    x = add([x, input])
    x = ReLU()(x)

    return x

The initial input is added to the output of the second 2D convolutional layer. The resultant tensor will be the output of the block.

2. Next, add a 2D convolutional block with the following hyperparameters:
   • Padding size: (1, 1)
   • Filters: 512
   • Kernel size: (3, 3)
   • Strides: 1
   • Batch normalization: Yes
   • Activation: ReLU

x = ZeroPadding2D(padding=(1, 1))(c_code)
x = Conv2D(512, kernel_size=(3, 3), strides=1, use_bias=False)(x)
x = BatchNormalization()(x)
x = ReLU()(x)

3. After that, add four residual blocks sequentially:

x = residual_block(x)
x = residual_block(x)
x = residual_block(x)
x = residual_block(x)

The upsampling blocks will receive this output tensor from the residual blocks. Let's write the code for the upsampling blocks.