code: def generate_train_batch(self):
      selected_keys = np.random.choice(
          self.list_of_keys, self.batch_size, True, None
      )
      data = np.zeros(self.data_shape, dtype=np.float32)
      seg = np.zeros(self.seg_shape, dtype=np.float32)
  
      case_properties = []
      for j, i in enumerate(selected_keys):
          if "properties" in self._data[i].keys():
              properties = self._data[i]["properties"]
          else:
              with open(self._data[i]["properties_file"], "rb") as f:
                  properties = pickle.load(
                      self._data[i]["properties_file"]
                  )
          case_properties.append(properties)
  
          if self.get_do_oversample(j):
              force_fg = True
          else:
              force_fg = False
  
          if not os.path.isfile(
              self._data[i]["data_file"][:-4] + ".npy"
          ):
              case_all_data = np.load(
                  self._data[i]["data_file"][:-4] + ".npz"
              )["data"]
          else:
              case_all_data = np.load(
                  self._data[i]["data_file"][:-4] + ".npy",
                  self.memmap_mode,
              )
  
          if len(case_all_data.shape) == 3:
              case_all_data = case_all_data[:, None]
  
          if not force_fg:
              random_slice = np.random.choice(case_all_data.shape[1])
              selected_class = None
          else:
              if "class_locations" not in properties.keys():
                  raise RuntimeError(
                      "Please rerun the preprocessing with the newest version of nnU-Net!"
                  )
  
              foreground_classes = np.array(
                  [
                      i
                      for i in properties["class_locations"].keys()
                      if len(properties["class_locations"][i]) != 0
                  ]
              )
              foreground_classes = foreground_classes[
                  foreground_classes > 0
              ]
              if len(foreground_classes) == 0:
                  selected_class = None
                  random_slice = np.random.choice(
                      case_all_data.shape[1]
                  )
                  print(
                      "case does not contain any foreground classes", i
                  )
              else:
                  selected_class = np.random.choice(foreground_classes)
  
                  voxels_of_that_class = properties["class_locations"][
                      selected_class
                  ]
                  valid_slices = np.unique(voxels_of_that_class[:, 0])
                  random_slice = np.random.choice(valid_slices)
                  voxels_of_that_class = voxels_of_that_class[
                      voxels_of_that_class[:, 0] == random_slice
                  ]
                  voxels_of_that_class = voxels_of_that_class[:, 1:]
  
          if self.pseudo_3d_slices == 1:
              case_all_data = case_all_data[:, random_slice]
          else:
              mn = random_slice - (self.pseudo_3d_slices - 1) // 2
              mx = random_slice + (self.pseudo_3d_slices - 1) // 2 + 1
              valid_mn = max(mn, 0)
              valid_mx = min(mx, case_all_data.shape[1])
              case_all_seg = case_all_data[-1:]
              case_all_data = case_all_data[:-1]
              case_all_data = case_all_data[:, valid_mn:valid_mx]
              case_all_seg = case_all_seg[:, random_slice]
              need_to_pad_below = valid_mn - mn
              need_to_pad_above = mx - valid_mx
              if need_to_pad_below > 0:
                  shp_for_pad = np.array(case_all_data.shape)
                  shp_for_pad[1] = need_to_pad_below
                  case_all_data = np.concatenate(
                      (np.zeros(shp_for_pad), case_all_data), 1
                  )
              if need_to_pad_above > 0:
                  shp_for_pad = np.array(case_all_data.shape)
                  shp_for_pad[1] = need_to_pad_above
                  case_all_data = np.concatenate(
                      (case_all_data, np.zeros(shp_for_pad)), 1
                  )
              case_all_data = case_all_data.reshape(
                  (-1, case_all_data.shape[-2], case_all_data.shape[-1])
              )
              case_all_data = np.concatenate(
                  (case_all_data, case_all_seg), 0
              )
  
          assert (
              len(case_all_data.shape) == 3
          ), "case data shape shoud be (c, x, y), but got {}.".format(
              case_all_data.shape
          )
  
          need_to_pad = self.need_to_pad.copy()
          for d in range(2):
              if (
                  need_to_pad[d] + case_all_data.shape[d + 1]
                  < self.patch_size[d]
              ):
                  need_to_pad[d] = (
                      self.patch_size[d] - case_all_data.shape[d + 1]
                  )
  
          shape = case_all_data.shape[1:]
          lb_x = -need_to_pad[0] // 2
          ub_x = (
              shape[0]
              + need_to_pad[0] // 2
              + need_to_pad[0] % 2
              - self.patch_size[0]
          )
          lb_y = -need_to_pad[1] // 2
          ub_y = (
              shape[1]
              + need_to_pad[1] // 2
              + need_to_pad[1] % 2
              - self.patch_size[1]
          )
  
          if not force_fg or selected_class is None:
              bbox_x_lb = np.random.randint(lb_x, ub_x + 1)
              bbox_y_lb = np.random.randint(lb_y, ub_y + 1)
          else:
              selected_voxel = voxels_of_that_class[
                  np.random.choice(len(voxels_of_that_class))
              ]
              bbox_x_lb = max(
                  lb_x, selected_voxel[0] - self.patch_size[0] // 2
              )
              bbox_y_lb = max(
                  lb_y, selected_voxel[1] - self.patch_size[1] // 2
              )
  
          bbox_x_ub = bbox_x_lb + self.patch_size[0]
          bbox_y_ub = bbox_y_lb + self.patch_size[1]
  
          valid_bbox_x_lb = max(0, bbox_x_lb)
          valid_bbox_x_ub = min(shape[0], bbox_x_ub)
          valid_bbox_y_lb = max(0, bbox_y_lb)
          valid_bbox_y_ub = min(shape[1], bbox_y_ub)
  
          case_all_data = case_all_data[
              :,
              valid_bbox_x_lb:valid_bbox_x_ub,
              valid_bbox_y_lb:valid_bbox_y_ub,
          ]
  
          case_all_data_donly = np.pad(
              case_all_data[:-1],
              (
                  (0, 0),
                  (-min(0, bbox_x_lb), max(bbox_x_ub - shape[0], 0)),
                  (-min(0, bbox_y_lb), max(bbox_y_ub - shape[1], 0)),
              ),
              self.pad_mode,
              **self.pad_kwargs_data,
          )
  
          case_all_data_segonly = np.pad(
              case_all_data[-1:],
              (
                  (0, 0),
                  (-min(0, bbox_x_lb), max(bbox_x_ub - shape[0], 0)),
                  (-min(0, bbox_y_lb), max(bbox_y_ub - shape[1], 0)),
              ),
              "constant",
              **{"constant_values": -1},
          )
  
          data[j] = case_all_data_donly
          seg[j] = case_all_data_segonly
  
      keys = selected_keys
      return {
          "data": data,
          "seg": seg,
          "properties": case_properties,
          "keys": keys,
      }
  
imports: import os
  import pickle
  import numpy
  from collections import OrderedDict
  from abc import abstractmethod
label_desc: Use image node_id 4 for calling the DataLoader2D obj's underlying member method code with example usage: obj.generate_train_batch() and returns: dict