class_docstr: A trial class which suggests a fixed value for each parameter.
  
  This object has the same methods as :class:`~optuna.trial.Trial`, and it suggests pre-defined
  parameter values. The parameter values can be determined at the construction of the
  :class:`~optuna.trial.FixedTrial` object. In contrast to :class:`~optuna.trial.Trial`,
  :class:`~optuna.trial.FixedTrial` does not depend on :class:`~optuna.study.Study`, and it is
  useful for deploying optimization results.
  
  Example:
  
      Evaluate an objective function with parameter values given by a user.
  
      .. testcode::
  
          import optuna
  
  
          def objective(trial):
              x = trial.suggest_float("x", -100, 100)
              y = trial.suggest_categorical("y", [-1, 0, 1])
              return x**2 + y
  
  
          assert objective(optuna.trial.FixedTrial({"x": 1, "y": 0})) == 1
  
  
  .. note::
      Please refer to :class:`~optuna.trial.Trial` for details of methods and properties.
  
  Args:
      params:
          A dictionary containing all parameters.
      number:
          A trial number. Defaults to ``0``.
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code: def __init__(self, params: Dict[str, Any], number: int = 0) -> None:
      self._params = params
      self._suggested_params: Dict[str, Any] = {}
      self._distributions: Dict[str, BaseDistribution] = {}
      self._user_attrs: Dict[str, Any] = {}
      self._system_attrs: Dict[str, Any] = {}
      self._datetime_start = datetime.datetime.now()
      self._number = number
  
imports: import datetime
  from typing import Any
  from typing import Dict
  from typing import Optional
  from typing import overload
  from typing import Sequence
  import warnings
  from optuna import distributions
  from optuna._convert_positional_args import convert_positional_args
  from optuna._deprecated import deprecated_func
  from optuna.distributions import BaseDistribution
  from optuna.distributions import CategoricalChoiceType
  from optuna.distributions import CategoricalDistribution
  from optuna.distributions import FloatDistribution
  from optuna.distributions import IntDistribution
  from optuna.trial._base import _SUGGEST_INT_POSITIONAL_ARGS
  from optuna.trial._base import BaseTrial
label_desc: Use image node_id 1 to create a new FixedTrial object from inherited base classes: BaseTrial with example: obj = FixedTrial(params, number)