In this section, we are going to implement a helper function that we'll define the model input placeholders which will responsible for feeding the data the the computational graph.

The functions should be able to create three main placeholders:

• Actual input images from the dataset which will have the dimensions of (batch size, input image width, input image height, number of channels)
• The latent space Z, which will be used by the generator for generating fake images
• Learning rate placeholder

The helper function will return a tuple of these three input placeholders. So, let's go ahead and define this function:

# defining the model inputs
def inputs(img_width, img_height, img_channels, latent_space_z_dim):
    true_inputs = tf.placeholder(tf.float32, (None, img_width, img_height, img_channels),
                                 'true_inputs')
    l_space_inputs = tf.placeholder(tf.float32, (None, latent_space_z_dim), 'l_space_inputs')
    model_learning_rate = tf.placeholder(tf.float32, name='model_learning_rate')

    return true_inputs, l_space_inputs, model_learning_rate