class_docstr: The ``InstanceSegmentation`` is a :class:`~flash.Task` for detecting objects in images. For more details, see
  :ref:`object_detection`.
  
  Args:
      num_classes: The number of object classes.
      backbone: String indicating the backbone CNN architecture to use.
      head: String indicating the head module to use on top of the backbone.
      pretrained: Whether the model should be loaded with it's pretrained weights.
      optimizer: Optimizer to use for training.
      lr_scheduler: The LR scheduler to use during training.
      learning_rate: The learning rate to use for training.
      predict_kwargs: dictionary containing parameters that will be used during the prediction phase.
      **kwargs: additional kwargs used for initializing the task
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docstr: The kwargs used for the prediction step.
docstr_tok: ["The","kwargs","used","for","the","prediction","step","."]
code: def predict_kwargs(self) -> Dict[str, Any]:
      """The kwargs used for the prediction step."""
      return self.adapter.predict_kwargs
  
file_path: lightning-flash/src/flash/image/instance_segmentation/model.py
imports: from typing import Any, Dict, List, Optional
  from flash.core.adapter import AdapterTask
  from flash.core.registry import FlashRegistry
  from flash.core.utilities.types import LR_SCHEDULER_TYPE, OPTIMIZER_TYPE, OUTPUT_TRANSFORM_TYPE
  from flash.image.instance_segmentation.backbones import INSTANCE_SEGMENTATION_HEADS
  from flash.image.instance_segmentation.data import InstanceSegmentationOutputTransform
label_desc: Use image node_id 3 for calling the InstanceSegmentation obj's underlying member method code with example usage: obj.predict_kwargs() and returns: self