code: def generate_samples() -> typing.List[MatmulSize]:
      """Generate a list of matmuls we will be benchmarking."""
      m_axis = np.unique(
          np.logspace(4, 13, num=200, dtype=np.int64, base=2)
      )
      n_axis = np.unique(
          np.logspace(4, 13, num=200, dtype=np.int64, base=2)
      )
      k_axis = np.unique(
          np.logspace(4, 13, num=200, dtype=np.int64, base=2)
      )
      quants = [QuantizedInputType.BFLOAT16, QuantizedInputType.INT8]
      prod = itertools.product(
          m_axis, n_axis, k_axis, quants[1], quants[0]
      )
      out = random.choices(
          (MatmulSize(*p) for p in prod), k=_NUM_SAMPLES.value
      )
      return out
  
imports: import concurrent.futures
  import csv
  import itertools
  import logging
  import os
  import random
  import sys
  import time
  import typing
  from absl import app
  from absl import flags
  from matmul_lib import benchmark_matmul
  from matmul_lib import generate_tiling_configs
  from matmul_lib import MatmulSize
  from matmul_lib import MatmulTiming
  from matmul_lib import parse_int_list
  from matmul_lib import parse_layout_list
  from matmul_lib import QuantizedInputType
  import numpy
  import torch
  import tqdm
  from tqdm.contrib.logging import logging_redirect_tqdm
label_desc: Use image node_id 4 for calling a global function with example usage: generate_samples() and returns: out