class_docstr: Represents the t-distribution value.
  
  It includes sample_mean, sample_standard_deviation,
  sample_degrees_of_freedom. And also unsampled_value is also stored here to
  record the value calculated without bootstrapping.
  The sample_standard_deviation is calculated as:
  \sqrt{ \frac{1}{N-1} \sum_{i=1}^{N}{(x_i - \bar{x})^2} }
class_docstr_tok: ["Represents","the","t-distribution","value",".","It","includes","sample_mean",",","sample_standard_deviation",",","sample_degrees_of_freedom",".","And","also","unsampled_value","is","also","stored","here","to","record","the","value","calculated","without","bootstrapping",".","The","sample_standard_deviation","is","calculated","as",":","\\sqrt","{","\\frac","{","1","}","{","N-1","}","\\sum_","{","i=1","}","^","{","N","}","{","(","x_i","-","\\bar","{","x","}",")","^2","}","}"]
code: def __float__(self):
      # unsampled_value can be numpy.float which is a subclass of float, but here
      # need to return a strict float.
      return float(self.unsampled_value)
  
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 ValueWithTDistribution obj's underlying member method code with example usage: obj.__float__() and returns: float