class_docstr: ## Configurations
  
  This extends MNIST configurations to get the data loaders and Training and validation loop
  configurations to simplify our implementation.
class_docstr_tok: ["#","#","Configurations","This","extends","MNIST","configurations","to","get","the","data","loaders","and","Training","and","validation","loop","configurations","to","simplify","our","implementation","."]
docstr: $$z \sim p(z)$$
docstr_tok: ["$","$","z","\\sim","p","(","z",")","$","$"]
code: def sample_z(self, batch_size: int):
      """
      $$z \sim p(z)$$
      """
      return torch.randn(batch_size, 100, device=self.device)
  
imports: from typing import Any
  import torch
  import torch.nn
  import torch.utils.data
  from torchvision import transforms
  from labml import tracker, monit, experiment
  from labml.configs import option, calculate
  from labml_helpers.datasets.mnist import MNISTConfigs
  from labml_helpers.device import DeviceConfigs
  from labml_helpers.module import Module
  from labml_helpers.optimizer import OptimizerConfigs
  from labml_helpers.train_valid import TrainValidConfigs, hook_model_outputs, BatchIndex
  from labml_nn.gan.original import DiscriminatorLogitsLoss, GeneratorLogitsLoss
label_desc: Use image node_id 2 for calling the Configs obj's underlying member method code with example usage: obj.sample_z(batch_size) and returns: torch