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.
docstr: Creates a `Dataset` from a list of images.
  
  # Arguments
      images: `Sequence` of images in memory (numpy arrays).
      tfm: Transforms to be applied to each item.
code: def from_images(
      cls,
      images: Sequence[np.array],
      tfm: Transform = None,
      class_map: Optional[ClassMap] = None,
  ):
      """Creates a `Dataset` from a list of images.
  
      # Arguments
          images: `Sequence` of images in memory (numpy arrays).
          tfm: Transforms to be applied to each item.
      """
      records = []
      for i, image in enumerate(images):
          record = BaseRecord((ImageRecordComponent(),))
          record.set_record_id(i)
          record.set_img(image)
          records.append(record)
  
          # TODO, HACK: adding class map because of `convert_raw_prediction`
          record.add_component(
              ClassMapRecordComponent(task=tasks.detection)
          )
          if class_map is not None:
              record.detection.set_class_map(class_map)
  
      return cls(records=records, 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 5 for calling the Dataset obj's underlying member method code with example usage: obj.from_images(cls, images, tfm, class_map) and returns: cls