class_docstr: Defines the cohort which will be injected from SDK into the Dashboard.
  :param name: Name of the cohort.
  :type name: str
class_docstr_tok: ["Defines","the","cohort","which","will","be","injected","from","SDK","into","the","Dashboard",".",":","param","name",":","Name","of","the","cohort",".",":","type","name",":","str"]
code: def __eq__(self, cohort: Any):
      same_name = self.name == cohort.name
      if (
          self.cohort_filter_list is None
          and cohort.cohort_filter_list is None
      ):
          return same_name
      elif (
          self.cohort_filter_list is not None
          and cohort.cohort_filter_list is None
      ):
          return False
      elif (
          self.cohort_filter_list is None
          and cohort.cohort_filter_list is not None
      ):
          return False
  
      same_num_cohort_filters = len(self.cohort_filter_list) == len(
          cohort.cohort_filter_list
      )
      if not same_num_cohort_filters:
          return False
  
      same_cohort_filters = True
      for index in range(0, len(self.cohort_filter_list)):
          if (
              self.cohort_filter_list[index]
              != cohort.cohort_filter_list[index]
          ):
              same_cohort_filters = False
              break
  
      return same_name and same_cohort_filters
  
imports: import json
  from typing import Any, List, Optional
  import numpy
  import pandas
  from raiutils.exceptions import UserConfigValidationException
  from .constants import ClassificationOutcomes, CohortFilterMethods
label_desc: Use image node_id 2 for calling the Cohort obj's underlying member method code with example usage: obj.__eq__(cohort) and returns: unknown, same_name, False, False, False