class_docstr: A trial is a process of evaluating an objective function.
  
  This object is passed to an objective function and provides interfaces to get parameter
  suggestion, manage the trial's state, and set/get user-defined attributes of the trial.
  
  Note that the direct use of this constructor is not recommended.
  This object is seamlessly instantiated and passed to the objective function behind
  the :func:`optuna.multi_objective.study.MultiObjectiveStudy.optimize()` method;
  hence library users do not care about instantiation of this object.
  
  Args:
      trial:
          A :class:`~optuna.trial.Trial` object.
docstr: Report intermediate objective function values for a given step.
  
  The reported values are used by the pruners to determine whether this trial should be
  pruned.
  
  .. seealso::
      Please refer to :class:`~optuna.pruners.BasePruner`.
  
  .. note::
      The reported values are converted to ``float`` type by applying ``float()``
      function internally. Thus, it accepts all float-like types (e.g., ``numpy.float32``).
      If the conversion fails, a ``TypeError`` is raised.
  
  Args:
      values:
          Intermediate objective function values for a given step.
      step:
          Step of the trial (e.g., Epoch of neural network training).
docstr_tok: ["Report","intermediate","objective","function","values","for","a","given","step",".","The","reported","values","are","used","by","the","pruners","to","determine","whether","this","trial","should","be","pruned",".","..","seealso",":",":","Please","refer","to",":","class",":","`","~optuna.pruners.BasePruner","`",".","..","note",":",":","The","reported","values","are","converted","to","``","float","``","type","by","applying","``","float","(",")","``","function","internally",".","Thus",",","it","accepts","all","float-like","types","(","e.g.",",","``","numpy.float32","``",")",".","If","the","conversion","fails",",","a","``","TypeError","``","is","raised",".","Args",":","values",":","Intermediate","objective","function","values","for","a","given","step",".","step",":","Step","of","the","trial","(","e.g.",",","Epoch","of","neural","network","training",")","."]
code: def report(self, values: Sequence[float], step: int) -> None:
      """Report intermediate objective function values for a given step.
  
      The reported values are used by the pruners to determine whether this trial should be
      pruned.
  
      .. seealso::
          Please refer to :class:`~optuna.pruners.BasePruner`.
  
      .. note::
          The reported values are converted to ``float`` type by applying ``float()``
          function internally. Thus, it accepts all float-like types (e.g., ``numpy.float32``).
          If the conversion fails, a ``TypeError`` is raised.
  
      Args:
          values:
              Intermediate objective function values for a given step.
          step:
              Step of the trial (e.g., Epoch of neural network training).
      """
  
      # TODO(ohta): Allow users reporting a subset of target values.
      # See https://github.com/optuna/optuna/pull/1054/files#r401594785 for the detail.
  
      if len(values) != self._n_objectives:
          raise ValueError(
              "The number of the intermediate values {} at step {} is mismatched with"
              "the number of the objectives {}.",
              len(values),
              step,
              self._n_objectives,
          )
  
      for i, value in enumerate(values):
          self._trial.report(value, self._n_objectives * (step + 1) + i)
  
imports: from datetime import datetime
  from typing import Any
  from typing import Dict
  from typing import List
  from typing import Optional
  from typing import overload
  from typing import Sequence
  from typing import Union
  from optuna import multi_objective
  from optuna._convert_positional_args import convert_positional_args
  from optuna._deprecated import deprecated_class
  from optuna.distributions import BaseDistribution
  from optuna.study._study_direction import StudyDirection
  from optuna.trial import FrozenTrial
  from optuna.trial import Trial
  from optuna.trial import TrialState
  from optuna.trial._base import _SUGGEST_INT_POSITIONAL_ARGS
label_desc: Use image node_id 14 for calling the MultiObjectiveTrial obj's underlying member method code with example usage: obj.report(values, step) without return types