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 __new__(
      cls,
      sample_mean: float,
      sample_standard_deviation: Optional[float] = None,
      sample_degrees_of_freedom: Optional[int] = None,
      unsampled_value: Optional[float] = None,
  ):
      return super(ValueWithTDistribution, cls).__new__(
          cls,
          sample_mean,
          sample_standard_deviation,
          sample_degrees_of_freedom,
          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 1 for calling the ValueWithTDistribution obj's underlying member method code with example usage: obj.__new__(cls, sample_mean, sample_standard_deviation, sample_degrees_of_freedom, unsampled_value) and returns: super