code: def input_fn(params):
      """Return a dataset of source and target sequences for training."""
      return (
          constant_op.constant(
              np.random.randn(params["batch_size"], 1000),
              dtype=dtypes.float32,
          ),
          constant_op.constant(
              np.random.randint(0, 10, params["batch_size"]),
              dtype=dtypes.int32,
          ),
      )
  
imports: import os
  import numpy
  from tensorflow.core.framework import summary_pb2
  from tensorflow.python.compat import v2_compat
  from tensorflow.python.distribute.cluster_resolver import tpu_cluster_resolver
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.lib.io import file_io
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import metrics
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops.losses import losses
  from tensorflow.python.platform import flags
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.saved_model.pywrap_saved_model import metrics
  from tensorflow.python.tpu import async_checkpoint
  from tensorflow.python.tpu import tpu_optimizer
  from tensorflow.python.training import basic_session_run_hooks
  from tensorflow.python.training import training
  from tensorflow_estimator.python.estimator import estimator
  from tensorflow_estimator.python.estimator import model_fn
  from tensorflow_estimator.python.estimator.tpu import tpu_config
  from tensorflow_estimator.python.estimator.tpu import tpu_estimator
label_desc: Use image node_id 2 for calling a global function with example usage: input_fn(params) and returns: constant_op,  constant_op