class_docstr: VarLenTensorValue encapsulates a batch of varlen dense tensor values.
  
  Attributes:
    values: A np.ndarray of values.
    indices: A np.ndarray of indices.
    dense_shape: A np.ndarray representing the dense shape of the entire tensor.
      Note that each row (i.e. set of values sharing the same value for the
      first / batch dimension) is considered to have its own shape based on the
      presence of values.
class_docstr_tok: ["VarLenTensorValue","encapsulates","a","batch","of","varlen","dense","tensor","values",".","Attributes",":","values",":","A","np.ndarray","of","values",".","indices",":","A","np.ndarray","of","indices",".","dense_shape",":","A","np.ndarray","representing","the","dense","shape","of","the","entire","tensor",".","Note","that","each","row","(","i.e",".","set","of","values","sharing","the","same","value","for","the","first","\/","batch","dimension",")","is","considered","to","have","its","own","shape","based","on","the","presence","of","values","."]
code: def dense_rows(self):
      return self.DenseRowIterator(self)
  
imports: import abc
  import datetime
  import operator
  from typing import Any, Callable, Dict, Iterable, List, MutableMapping, NamedTuple, Optional, TypeVar, Tuple, Union
  from apache_beam.utils import shared
  import numpy
  import six
  import tensorflow
  from tensorflow_model_analysis.proto import metrics_for_slice_pb2
label_desc: Use image node_id 2 for calling the VarLenTensorValue obj's underlying member method code with example usage: obj.dense_rows() and returns: self