code: def test_pandas_batcher_use_all_samples():
      df = pd.DataFrame(
          {
              "name": ["joe", "janice", "sara"],
              "mask": ["green", "black", "pink"],
              "weapon": ["stick", "gun", "gun"],
          }
      )
      config = yaml.safe_load(
          """
      model_type: llm
      base_model: HuggingFaceH4/tiny-random-LlamaForCausalLM
      input_features:
      - name: name
        type: text
        preprocessing:
          max_sequence_length: 256
        column: name
      output_features:
      - name: weapon
        type: text
        preprocessing:
          max_sequence_length: 256
        column: weapon
      preprocessing:
        split:
          type: random
          probabilities:
            - 1
            - 0
            - 0
      """
      )
      model = LudwigModel(config=config, logging_level=logging.INFO)
      data = model.preprocess(df, skip_save_processed_input=False)
      training_set = data[0]
      features = training_set.dataset.keys()
  
      batches = []
      with training_set.initialize_batcher(batch_size=1) as batcher:
          while not batcher.last_batch():
              batch = batcher.next_batch()
              batches.append(batch)
      assert (len(batches)) == training_set.size
  
      # Check to see if all items are used exactly once
      for feature in features:
          for i in range(len(training_set.dataset[feature])):
              # Each of the arrays in the line below should contain the vector representation of a feature of sample i
              assert (
                  batches[i][feature].squeeze()
                  == training_set.dataset[feature][i].squeeze()
              ).all()
  
      # Check if string loading works as well
      batches = []
      # data[0].data_hdf5_fp is the string filepath to the cached data from preprocessing
      data_from_str = PandasDataset(
          data[0].data_hdf5_fp, data[0].features, None
      )
      features = data_from_str.dataset.keys()
  
      with data_from_str.initialize_batcher(batch_size=1) as batcher:
          while not batcher.last_batch():
              batch = batcher.next_batch()
              batches.append(batch)
      assert (len(batches)) == data_from_str.size
  
      # Check to see if all items are used exactly once
      for feature in features:
          for i in range(len(data_from_str.dataset[feature])):
              # Each of the arrays in the line below should contain the vector representation of a feature of sample i
              assert (
                  batches[i][feature].squeeze()
                  == data_from_str.dataset[feature][i].squeeze()
              ).all()
  
imports: import logging
  import pandas
  import yaml
  from ludwig.api import LudwigModel
  from ludwig.data.dataset.pandas import PandasDataset
label_desc: Use image node_id 2 for calling a global function with example usage: test_pandas_batcher_use_all_samples() without return types