class_docstr: The ``ImageEmbedder`` is a :class:`~flash.Task` for obtaining feature vectors (embeddings) from images. For more
  details, see :ref:`image_embedder`.
  
  Args:
      training_strategy: Training strategy from VISSL,
          select between 'simclr', 'swav', or 'barlow_twins'.
      head: projection head used for task, select between
          'simclr_head', 'swav_head', or 'barlow_twins_head'.
      pretraining_transform: transform applied to input image for pre-training SSL model.
          Select between 'simclr_transform', 'swav_transform', or 'barlow_twins_transform'.
      backbone: VISSL backbone, defaults to ``resnet``.
      pretrained: Use a pretrained backbone, defaults to ``False``.
      optimizer: Optimizer to use for training.
      lr_scheduler: The LR scheduler to use during training.
      learning_rate: Learning rate to use for training, defaults to ``1e-3``.
      backbone_kwargs: arguments to be passed to VISSL backbones, i.e. ``vision_transformer`` and ``resnet``.
      training_strategy_kwargs: arguments passed to VISSL loss function, projection head and training hooks.
      pretraining_transform_kwargs: arguments passed to VISSL transforms.
code: def predict_step(
      self, batch: Any, batch_idx: int, dataloader_idx: int = 0
  ) -> Any:
      return self(batch[DataKeys.INPUT])
  
imports: import warnings
  from typing import Any, Dict, List, Optional
  from pytorch_lightning.utilities import rank_zero_warn
  from torch import Tensor
  from flash.core.adapter import AdapterTask
  from flash.core.data.io.input import DataKeys
  from flash.core.registry import FlashRegistry
  from flash.core.utilities.imports import _VISSL_AVAILABLE, requires
  from flash.core.utilities.types import LR_SCHEDULER_TYPE, OPTIMIZER_TYPE
  from flash.image.classification.backbones import IMAGE_CLASSIFIER_BACKBONES
  from flash.image.embedding.strategies import IMAGE_EMBEDDER_STRATEGIES
  from flash.image.embedding.transforms import IMAGE_EMBEDDER_TRANSFORMS
label_desc: Use image node_id 3 for calling the ImageEmbedder obj's underlying member method code with example usage: obj.predict_step(batch, batch_idx, dataloader_idx) and returns: self