class_docstr: Agents than plays through a torch neural network
class_docstr_tok: ["Agents","than","plays","through","a","torch","neural","network"]
code: def __init__(
      self,
      module: nn.Module,
      deterministic: bool = True,
      instrumentation_std: float = 0.1,
  ) -> None:
      super().__init__()
      self.deterministic = deterministic
      self.module = module
      kwargs = {
          name: p.Array(shape=value.shape)
          .set_mutation(sigma=instrumentation_std)
          .set_bounds(-10, 10, method="arctan")
          for name, value in module.state_dict().items()  # type: ignore
      }  # bounded to avoid overflows
      self.instrumentation = p.Instrumentation(**kwargs)
  
imports: import warnings
  import operator
  import copy
  import typing
  import gym
  import numpy
  from nevergrad.common.tools import pytorch_import_fix
  from nevergrad.parametrization import parameter
  from ..base import ExperimentFunction
  from .None import base
  from .None import envs
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.utils.data import WeightedRandomSampler
label_desc: Use image node_id 1 to create a new TorchAgent object from inherited base classes: base with example: obj = TorchAgent(module, deterministic, instrumentation_std)