code: def random_batch(batch_size):
      images = np.random.rand(*image_shape(batch_size)).astype(
          np.float32
      )
      num_classes = 1000
      labels = np.random.randint(
          low=0, high=num_classes, size=[batch_size]
      ).astype(np.int32)
      one_hot = np.zeros((batch_size, num_classes)).astype(np.float32)
      one_hot[np.arange(batch_size), labels] = 1.0
      return images, one_hot
  
file_path: tensorflow/tensorflow/python/eager/benchmarks/resnet50/resnet50_graph_test.py
imports: import time
  import numpy
  import tensorflow.compat.v1
  from tensorflow.python.eager.benchmarks.resnet50 import resnet50
label_desc: Use image node_id 3 for calling a global function with example usage: random_batch(batch_size) and returns: images,  one_hot