class_docstr: A transform that returns concatenated sine and cosine features.
class_docstr_tok: ["A","transform","that","returns","concatenated","sine","and","cosine","features","."]
docstr: Initializes a SineCosineTransform instance.
  
  Args:
      scale: An optional tensor used to rescale the module's outputs.
docstr_tok: ["Initializes","a","SineCosineTransform","instance",".","Args",":","scale",":","An","optional","tensor","used","to","rescale","the","module","'s","outputs","."]
code: def __init__(self, scale: Optional[Tensor] = None):
      r"""Initializes a SineCosineTransform instance.
  
      Args:
          scale: An optional tensor used to rescale the module's outputs.
      """
      super().__init__()
      self.scale = scale
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Any, Callable, Iterable, List, Optional, overload, Tuple, Union
  import torch
  from botorch.models.approximate_gp import SingleTaskVariationalGP
  from botorch.models.gpytorch import GPyTorchModel
  from botorch.models.model import Model, ModelList
  from botorch.models.transforms.input import InputTransform
  from botorch.models.transforms.outcome import OutcomeTransform
  from botorch.utils.dispatcher import Dispatcher
  from gpytorch.kernels import ScaleKernel
  from gpytorch.kernels.kernel import Kernel
  from torch import LongTensor, Tensor
  from torch.nn import Module, ModuleList
label_desc: Use image node_id 1 to create a new SineCosineTransform object from inherited base classes: TensorTransform with example: obj = SineCosineTransform(scale)