class_docstr: Composes several transforms together.
  
  This transform does not support torchscript.
  Please, see the note below.
  
  Args:
      transforms (list of ``Transform`` objects): list of transforms to compose.
  
  Example:
      >>> transforms.Compose([
      >>>     transforms.CenterCrop(10),
      >>>     transforms.PILToTensor(),
      >>>     transforms.ConvertImageDtype(torch.float),
      >>> ])
  
  .. note::
      In order to script the transformations, please use ``torch.nn.Sequential`` as below.
  
      >>> transforms = torch.nn.Sequential(
      >>>     transforms.CenterCrop(10),
      >>>     transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
      >>> )
      >>> scripted_transforms = torch.jit.script(transforms)
  
      Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require
      `lambda` functions or ``PIL.Image``.
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code: def forward(self, *inputs: Any) -> Any:
      needs_unpacking = len(inputs) > 1
      for transform in self.transforms:
          outputs = transform(*inputs)
          inputs = outputs if needs_unpacking else (outputs,)
      return outputs
  
imports: from typing import Any, Callable, Dict, List, Optional, Sequence, Union
  import torch
  from torch import nn
  from torchvision import transforms
  from torchvision.transforms.v2 import Transform
label_desc: Use image node_id 2 for calling the Compose obj's underlying member method code with example usage: obj.forward() and returns: outputs