class_docstr: ## Configurations
class_docstr_tok: ["#","#","Configurations"]
code: def init(self):
      # Initialize the [Carvana dataset](carvana.html)
      self.dataset = CarvanaDataset(
          lab.get_data_path() / "carvana" / "train",
          lab.get_data_path() / "carvana" / "train_masks",
      )
      # Initialize the model
      self.model = UNet(self.image_channels, self.mask_channels).to(
          self.device
      )
  
      # Create dataloader
      self.data_loader = torch.utils.data.DataLoader(
          self.dataset, self.batch_size, shuffle=True, pin_memory=True
      )
      # Create optimizer
      self.optimizer = torch.optim.Adam(
          self.model.parameters(), lr=self.learning_rate
      )
  
      # Image logging
      tracker.set_image("sample", True)
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/unet/experiment.py
imports: import numpy
  import torch
  import torch.utils.data
  import torchvision.transforms.functional
  from torch import nn
  from labml import lab, tracker, experiment, monit
  from labml.configs import BaseConfigs
  from labml_helpers.device import DeviceConfigs
  from labml_nn.unet.carvana import CarvanaDataset
  from labml_nn.unet import UNet
label_desc: Use image node_id 1 for calling the Configs obj's underlying member method code with example usage: obj.init() without return types