code: def _sparse_tensor_dense_vs_dense_matmul_benchmark_dense(
      x, y, adjoint_a, adjoint_b
  ):
      def body(t, prev):
          with ops.control_dependencies([prev]):
              return (
                  t + 1,
                  math_ops.matmul(
                      x,
                      y,
                      transpose_a=adjoint_a,
                      transpose_b=adjoint_b,
                      a_is_sparse=True,
                      b_is_sparse=False,
                  ),
              )
  
      t0 = constant_op.constant(0)
      v0 = constant_op.constant(0.0)
  
      def _timeit(iterations, _):
          (_, final) = while_loop.while_loop(
              lambda t, _: t < iterations,
              body,
              (t0, v0),
              parallel_iterations=1,
              back_prop=False,
              shape_invariants=(
                  tensor_shape.TensorShape(()),
                  tensor_shape.TensorShape(None),
              ),
          )
          return [final]
  
      return _timeit
  
imports: import sys
  import time
  from absl import app
  import numpy
  from tensorflow.core.protobuf import config_pb2
  from tensorflow.python.client import session
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.ops import while_loop
  from tensorflow.python.platform import test
label_desc: Use image node_id 2 for calling a global function with example usage: _sparse_tensor_dense_vs_dense_matmul_benchmark_dense(x, y, adjoint_a, adjoint_b) and returns: _timeit, list, unknown,  math_ops