class_docstr: Sequentially composes multiple transforms. This is the Hybrid version of Compose.
  
  Parameters
  ----------
  transforms : list of transform Blocks.
      The list of transforms to be composed.
  
  
  Inputs:
      - **data**: input tensor with shape of the first transform Block requires.
  
  Outputs:
      - **out**: output tensor with shape of the last transform Block produces.
  
  Examples
  --------
  >>> transformer = transforms.HybridCompose([transforms.Resize(300),
  ...                                   transforms.CenterCrop(256),
  ...                                   transforms.ToTensor()])
  >>> image = mx.nd.random.uniform(0, 255, (224, 224, 3)).astype(dtype=np.uint8)
  >>> transformer(image)
  <NDArray 3x256x256 @cpu(0)>
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code: def __init__(self, transforms):
      super(HybridCompose, self).__init__()
      for i in transforms:
          if not isinstance(i, HybridBlock):
              raise ValueError(
                  "{} is not a HybridBlock, try use `Compose` instead".format(
                      i
                  )
              )
          self.add(i)
      self.hybridize()
  
imports: import warnings
  import random
  from ....block import Block, HybridBlock
  from ....nn import Sequential, HybridSequential
  from .....util import use_np
  from .....None import np, npx
  from .image import *
  from .image import _append_return
label_desc: Use image node_id 1 to create a new HybridCompose object from inherited base classes: HybridSequential with example: obj = HybridCompose(transforms)