code: def _get_sparse_tensors(self, dtype=dtypes.int32):
      feature0 = sparse_tensor.SparseTensor(
          indices=self.feature_watched_indices,
          values=constant_op.constant(
              self.feature_watched_values, dtype=dtype
          ),
          dense_shape=[self.data_batch_size, 2],
      )
      feature1 = sparse_tensor.SparseTensor(
          indices=self.feature_favorited_indices,
          values=constant_op.constant(
              self.feature_favorited_values, dtype=dtype
          ),
          dense_shape=[self.data_batch_size, 2],
      )
      feature2 = sparse_tensor.SparseTensor(
          indices=self.feature_friends_indices,
          values=constant_op.constant(
              self.feature_friends_values, dtype=dtype
          ),
          dense_shape=[self.data_batch_size, 3],
      )
      return (feature0, feature1, feature2)
  
imports: import numpy
  from tensorflow.python.compat import v2_compat
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.ops import init_ops_v2
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.platform import test
  from tensorflow.python.tpu import tpu_embedding_for_serving
  from tensorflow.python.tpu import tpu_embedding_v2_utils
  from tensorflow.python.util import nest
label_desc: Use image node_id 6 for calling the TPUEmbeddingForServingTest obj's underlying member method code with example usage: obj._get_sparse_tensors(dtype) and returns: feature0,  feature1,  feature2