class_docstr: VAE with linear Normalizing Flow config class.
  
  Parameters:
      input_dim (int): The input_data dimension
      latent_dim (int): The latent space dimension. Default: None.
      reconstruction_loss (str): The reconstruction loss to use ['bce', 'mse']. Default: 'mse'
      flows (List[str]):  A list of strings corresponding to the class of each flow to be applied.
          Default: ['Plannar', 'Planar']. Flow choices: ['Planar', 'Radial'].
class_docstr_tok: ["VAE","with","linear","Normalizing","Flow","config","class",".","Parameters",":","input_dim","(","int",")",":","The","input_data","dimension","latent_dim","(","int",")",":","The","latent","space","dimension",".","Default",":","None",".","reconstruction_loss","(","str",")",":","The","reconstruction","loss","to","use","[","'bce","'",",","'mse","'","]",".","Default",":","'mse'","flows","(","List","[","str","]",")",":","A","list","of","strings","corresponding","to","the","class","of","each","flow","to","be","applied",".","Default",":","[","'Plannar","'",",","'Planar","'","]",".","Flow","choices",":","[","'Planar","'",",","'Radial","'","]","."]
code: def __post_init__(self):
      super().__post_init__()
      for i, f in enumerate(self.flows):
          assert f in ["Planar", "Radial"], (
              f"Flow name number {i+1}: '{f}' doesn't correspond "
              "to ones of the classes. Available linear flows ['Planar', 'Radial']"
          )
  
imports: from dataclasses import field
  from typing import List
  from pydantic.dataclasses import dataclass
  from ..vae import VAEConfig
label_desc: Use image node_id 1 for calling the VAE_LinNF_Config obj's underlying member method code with example usage: obj.__post_init__() without return types