class_docstr: Benchmarks for constrained sequence ops in TF Text.
class_docstr_tok: ["Benchmarks","for","constrained","sequence","ops","in","TF","Text","."]
code: def load_input_data(self, batch_size):
      scores = [[10.0, 12.0, 6.0, 4.0], [13.0, 12.0, 11.0, 10.0]]
  
      self.input_data = constant_op.constant(
          [scores, scores, scores], dtype=np.float32
      )
      self.transition_weights = constant_op.constant(
          [
              [0.1, 0.2, 0.3, 0.4, 0.1],
              [0.5, 0.6, 0.7, 0.8, 0.1],
              [0.9, 1, 0.15, 1, 0.1],
              [0.25, 0.35, 0.45, 0.55, 0.5],
              [0.1, 0.5, 0.1, 0.1, 1],
          ],
          dtype=np.float32,
      )
  
      self.allowed_transitions = constant_op.constant(
          [
              [True, True, True, True, True],
              [True, True, True, True, True],
              [True, False, True, False, True],
              [True, True, True, True, True],
              [True, True, True, True, False],
          ]
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl import app
  from absl import flags
  import numpy
  import tensorflow
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow_text.python import ops
  from tensorflow_text.python.benchmarks import benchmark_utils
label_desc: Use image node_id 2 for calling the ConstrainedSequenceOpsBenchmark obj's underlying member method code with example usage: obj.load_input_data(batch_size) without return types