code: def benchmark_map_filter():
      times = {"num examples": SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
          features = datasets.Features(
              {
                  "text": datasets.Value("string"),
                  "numbers": datasets.Value("float32"),
              }
          )
          dataset = generate_example_dataset(
              os.path.join(tmp_dir, "dataset.arrow"),
              features,
              num_examples=SPEED_TEST_N_EXAMPLES,
          )
  
          tokenizer = transformers.AutoTokenizer.from_pretrained(
              "bert-base-cased", use_fast=True
          )
  
          def tokenize(examples):
              return tokenizer(examples["text"])
  
          times["map identity"] = map(dataset)
  
          times["map identity batched"] = map(dataset, batched=True)
  
          times["map no-op batched"] = map(
              dataset, function=lambda x: None, batched=True
          )
  
          with dataset.formatted_as(type="numpy"):
              times["map no-op batched numpy"] = map(
                  dataset, function=lambda x: None, batched=True
              )
  
          with dataset.formatted_as(type="pandas"):
              times["map no-op batched pandas"] = map(
                  dataset, function=lambda x: None, batched=True
              )
  
          with dataset.formatted_as(type="torch", columns="numbers"):
              times["map no-op batched pytorch"] = map(
                  dataset, function=lambda x: None, batched=True
              )
  
          with dataset.formatted_as(
              type="tensorflow", columns="numbers"
          ):
              times["map no-op batched tensorflow"] = map(
                  dataset, function=lambda x: None, batched=True
              )
  
          times["map fast-tokenizer batched"] = map(
              dataset, function=tokenize, batched=True
          )
  
          times["filter"] = filter(dataset)
  
          # Activate later when tokenizer support batched inputs
          # with dataset.formatted_as(type='numpy'):
          #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
  
      with open(RESULTS_FILE_PATH, "wb") as f:
          f.write(json.dumps(times).encode("utf-8"))
  
file_path: datasets/benchmarks/benchmark_map_filter.py
imports: import json
  import os
  import tempfile
  import transformers
  import datasets
  from utils import generate_example_dataset, get_duration
label_desc: Use image node_id 3 for calling a global function with example usage: benchmark_map_filter() and returns: tokenizer