docstr: :param framework_type: deep learning framework type. currently only tensorflow is supported
  :param evaluate_only: if not None, the task will be used only for evaluating the model for the given number of steps.
                          A value of 0 means that task will be evaluated for an infinite number of steps.
  :param use_cpu: use the cpu for this task
  :param experiment_path: the path to the directory which will store all the experiment outputs
  :param seed: a seed to use for the random numbers generator
  :param checkpoint_save_secs: the number of seconds between each checkpoint saving
  :param checkpoint_restore_dir:
          [DEPECRATED - will be removed in one of the next releases - switch to checkpoint_restore_path]
          the dir to restore the checkpoints from
  :param checkpoint_restore_path: the path to restore the checkpoints from
  :param checkpoint_save_dir: the directory to store the checkpoints in
  :param export_onnx_graph: If set to True, this will export an onnx graph each time a checkpoint is saved
  :param apply_stop_condition: If set to True, this will apply the stop condition defined by reaching a target success rate
  :param num_gpu: number of GPUs to use
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code: def __init__(
      self,
      framework_type: Frameworks = Frameworks.tensorflow,
      evaluate_only: int = None,
      use_cpu: bool = False,
      experiment_path="/tmp",
      seed=None,
      checkpoint_save_secs=None,
      checkpoint_restore_dir=None,
      checkpoint_restore_path=None,
      checkpoint_save_dir=None,
      export_onnx_graph: bool = False,
      apply_stop_condition: bool = False,
      num_gpu: int = 1,
  ):
      """
      :param framework_type: deep learning framework type. currently only tensorflow is supported
      :param evaluate_only: if not None, the task will be used only for evaluating the model for the given number of steps.
                              A value of 0 means that task will be evaluated for an infinite number of steps.
      :param use_cpu: use the cpu for this task
      :param experiment_path: the path to the directory which will store all the experiment outputs
      :param seed: a seed to use for the random numbers generator
      :param checkpoint_save_secs: the number of seconds between each checkpoint saving
      :param checkpoint_restore_dir:
              [DEPECRATED - will be removed in one of the next releases - switch to checkpoint_restore_path]
              the dir to restore the checkpoints from
      :param checkpoint_restore_path: the path to restore the checkpoints from
      :param checkpoint_save_dir: the directory to store the checkpoints in
      :param export_onnx_graph: If set to True, this will export an onnx graph each time a checkpoint is saved
      :param apply_stop_condition: If set to True, this will apply the stop condition defined by reaching a target success rate
      :param num_gpu: number of GPUs to use
      """
      self.framework_type = framework_type
      self.task_index = 0  # TODO: not really needed
      self.evaluate_only = evaluate_only
      self.use_cpu = use_cpu
      self.experiment_path = experiment_path
      self.checkpoint_save_secs = checkpoint_save_secs
      if checkpoint_restore_dir:
          screen.warning(
              "TaskParameters.checkpoint_restore_dir is DEPECRATED and will be removed in one of the next "
              "releases. Please switch to using TaskParameters.checkpoint_restore_path, with your "
              "directory path. "
          )
          self.checkpoint_restore_path = checkpoint_restore_dir
      else:
          self.checkpoint_restore_path = checkpoint_restore_path
      self.checkpoint_save_dir = checkpoint_save_dir
      self.seed = seed
      self.export_onnx_graph = export_onnx_graph
      self.apply_stop_condition = apply_stop_condition
      self.num_gpu = num_gpu
  
imports: import inspect
  import json
  import os
  import sys
  import types
  from collections import OrderedDict
  from enum import Enum
  from typing import Dict, List, Union
  from rl_coach.core_types import TrainingSteps, EnvironmentSteps, GradientClippingMethod, RunPhase, SelectedPhaseOnlyDumpFilter, MaxDumpFilter
  from rl_coach.filters.filter import NoInputFilter
  from rl_coach.logger import screen
label_desc: Use image node_id 1 to create a new TaskParameters object from inherited base classes: Parameters with example: obj = TaskParameters(framework_type, evaluate_only, use_cpu, experiment_path, seed, checkpoint_save_secs, checkpoint_restore_dir, checkpoint_restore_path, checkpoint_save_dir, export_onnx_graph, apply_stop_condition, num_gpu)