class_docstr: Derive a specific sub-class of a VAE for a CNN sentence model. 
class_docstr_tok: ["Derive","a","specific","sub-class","of","a","VAE","for","a","CNN","sentence","model","."]
code: def analyse(self, data, runPath, epoch):
      zemb, zsl, kls_df = super(CUB_Image_ft, self).analyse(data, K=10)
      labels = ["Prior", self.modelName.lower()]
      plot_embeddings(
          zemb,
          zsl,
          labels,
          "{}/emb_umap_{:03d}.png".format(runPath, epoch),
      )
      plot_kls_df(
          kls_df, "{}/kl_distance_{:03d}.png".format(runPath, epoch)
      )
  
imports: import torch
  import torch.distributions
  import torch.nn
  import torch.nn.functional
  import torch.utils.data
  from numpy import sqrt
  from torchvision.utils import make_grid, save_image
  from datasets import CUBImageFt
  from utils import Constants, NN_lookup
  from vis import plot_embeddings, plot_kls_df
  from .vae import VAE
label_desc: Use image node_id 6 for calling the CUB_Image_ft obj's underlying member method code with example usage: obj.analyse(data, runPath, epoch) without return types