code: def __init__(self, num_genes, z2_dim, hidden_dims):
      super().__init__()
      dims = [z2_dim] + hidden_dims + [2 * num_genes]
      self.fc = make_fc(dims)
  
imports: import argparse
  import matplotlib.pyplot
  import numpy
  import torch
  import torch.nn
  from matplotlib.patches import Patch
  from torch.distributions import constraints
  from torch.nn.functional import softmax, softplus
  from torch.optim import Adam
  import pyro
  import pyro.distributions
  import pyro.poutine
  from pyro.contrib.examples.scanvi_data import get_data
  from pyro.distributions.util import broadcast_shape
  from pyro.infer import SVI, TraceEnum_ELBO, config_enumerate
  from pyro.optim import MultiStepLR
label_desc: Use image node_id 1 to create a new XDecoder object from inherited base classes: nn with example: obj = XDecoder(num_genes, z2_dim, hidden_dims)