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def gan_model_optimizers(d_loss, g_loss, disc_lr, gen_lr, beta1):
    """
    Get optimization operations
    
    Arguments:
    ----------
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param disc_lr: Placeholder for Learning Rate for discriminator
    :param gen_lr: Placeholder for Learning Rate for generator

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def gan_model_loss(input_real, input_z, output_channel_dim, alpha):
    """
    Get the loss for the discriminator and generator
    
    Arguments:
    ---------
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    ---------

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# Define the directory with real image data
data_dir = './data/' # Data
resized_data_dir = "./resized_data" # folder for saving resized data

# Resize images into 128x128
preprocess = True # set to False if no resizing

if preprocess == True:
    # Create resized folder if not exist
    if not os.path.exists(resized_data_dir):

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def gan_model_inputs(real_dim, z_dim):
    """
    Creates the inputs for the model.
    
    Arguments:
    ----------
    :param real_dim: tuple containing width, height and channels
    :param z_dim: The dimension of Z
    ----------
    Returns:

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def generator(z, output_channel_dim, is_train=True):
    ''' Building the generator network.
    
        Arguments
        ---------
        z : Input tensor for the generator
        output_channel_dim : Shape of the generator output
        n_units : Number of units in hidden layer
        reuse : Reuse the variables with tf.variable_scope
        alpha : leak parameter for leaky ReLU

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def discriminator(x, is_reuse=False, alpha = 0.2):
    ''' Build the discriminator network.
    
        Arguments
        ---------
        x : Input tensor for the discriminator
        n_units: Number of units in hidden layer
        reuse : Reuse the variables with tf.variable_scope
        alpha : leak parameter for leaky ReLU
        

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