code: def test_prediction_loop_with_iterable_dataset(tmp_path):
      class MyModel(BoringModel):
          def predict_step(self, batch, batch_idx, dataloader_idx=0):
              return (batch, batch_idx, dataloader_idx)
  
      model = MyModel()
      trainer = Trainer(
          default_root_dir=tmp_path,
          limit_predict_batches=3,
          enable_model_summary=False,
          enable_checkpointing=False,
          logger=False,
          devices=1,
      )
      preds = trainer.predict(model, itertools.count())
      assert preds == [(0, 0, 0), (1, 1, 0), (2, 2, 0)]
  
      preds = trainer.predict(
          model, [itertools.count(), itertools.count()]
      )
      assert preds == [
          [(0, 0, 0), (1, 1, 0), (2, 2, 0)],
          [(0, 0, 1), (1, 1, 1), (2, 2, 1)],
      ]
  
      preds = trainer.predict(model, {"a": [0, 1], "b": [2, 3]})
      assert preds == [[(0, 0, 0), (1, 1, 0)], [(2, 0, 1), (3, 1, 1)]]
  
      preds = trainer.predict(model, [[0, 1], [2, 3]])
      assert preds == [[(0, 0, 0), (1, 1, 0)], [(2, 0, 1), (3, 1, 1)]]
  
      class MyModel(LightningModule):
          batch_start_ins = []
          step_outs = []
          batch_end_ins = []
  
          def on_predict_batch_start(
              self, batch, batch_idx, dataloader_idx
          ):
              self.batch_start_ins.append(
                  (batch, batch_idx, dataloader_idx)
              )
  
          def predict_step(self, dataloader_iter):
              self.step_outs.append(next(dataloader_iter))
  
          def on_predict_batch_end(
              self, outputs, batch, batch_idx, dataloader_idx
          ):
              self.batch_end_ins.append(
                  (batch, batch_idx, dataloader_idx)
              )
  
      model = MyModel()
      trainer.predict(model, {"a": [0, 1], "b": [2, 3]})
  
      assert (
          model.batch_start_ins == [(None, 0, 0)] + model.step_outs[:-1]
      )
      assert model.step_outs == [
          (0, 0, 0),
          (1, 1, 0),
          (2, 0, 1),
          (3, 1, 1),
      ]
      assert model.batch_end_ins == model.step_outs
  
file_path: pytorch-lightning/tests/tests_pytorch/loops/test_prediction_loop.py
imports: import itertools
  import pytest
  from lightning.pytorch import LightningModule, Trainer
  from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
  from lightning.pytorch.overrides.distributed import _IndexBatchSamplerWrapper
  from torch.utils.data import DataLoader, DistributedSampler, SequentialSampler
label_desc: Use image node_id 3 for calling a global function with example usage: test_prediction_loop_with_iterable_dataset(tmp_path) and returns: batch,  batch_idx,  dataloader_idx