class_docstr: Container for a list of records and transforms.
  
  Steps each time an item is requested (normally via directly indexing the `Dataset`):
      * Grab a record from the internal list of records.
      * Prepare the record (open the image, open the mask, add metadata).
      * Apply transforms to the record.
  
  # Arguments
      records: A list of records.
      tfm: Transforms to be applied to each item.
code: def __init__(
      self,
      records: List[dict],
      tfm: Optional[Transform] = None,
  ):
      self.records = records
      self.tfm = tfm
  
file_path: icevision/icevision/data/dataset.py
imports: from icevision.imports import *
  from icevision.core import *
  from icevision.tfms import *
label_desc: Use image node_id 1 to create a new Dataset object  with example: obj = Dataset(records, tfm)