class_docstr: Describes backbone information and provides utility functions.
      
class_docstr_tok: ["Describes","backbone","information","and","provides","utility","functions","."]
docstr: Download pre-trained weights for the specified backbone name.
  This name is in the format {backbone}_weights_tf_dim_ordering_tf_kernels_notop
  where backbone is the densenet + number of layers (e.g. densenet121).
  For more info check the explanation from the keras densenet script itself:
      https://github.com/keras-team/keras/blob/master/keras/applications/densenet.py
docstr_tok: ["Download","pre-trained","weights","for","the","specified","backbone","name",".","This","name","is","in","the","format","{","backbone","}","_weights_tf_dim_ordering_tf_kernels_notop","where","backbone","is","the","densenet","+","number","of","layers","(","e.g",".","densenet121",")",".","For","more","info","check","the","explanation","from","the","keras","densenet","script","itself",":","https",":","\/\/github.com\/keras-team\/keras\/blob\/master\/keras\/applications\/densenet.py"]
code: def download_imagenet(self):
      """Download pre-trained weights for the specified backbone name.
      This name is in the format {backbone}_weights_tf_dim_ordering_tf_kernels_notop
      where backbone is the densenet + number of layers (e.g. densenet121).
      For more info check the explanation from the keras densenet script itself:
          https://github.com/keras-team/keras/blob/master/keras/applications/densenet.py
      """
      origin = "https://github.com/fchollet/deep-learning-models/releases/download/v0.8/"
      file_name = "{}_weights_tf_dim_ordering_tf_kernels_notop.h5"
  
      # load weights
      if keras.backend.image_data_format() == "channels_first":
          raise ValueError(
              'Weights for "channels_first" format are not available.'
          )
  
      weights_url = origin + file_name.format(self.backbone)
      return keras.utils.get_file(
          file_name.format(self.backbone),
          weights_url,
          cache_subdir="models",
      )
  
file_path: ImageAI/imageai_tf_deprecated/Detection/keras_retinanet/models/densenet.py
imports: from tensorflow import keras
  from .None import retinanet
  from .None import Backbone
  from ..utils.image import preprocess_image
label_desc: Use image node_id 2 for calling the DenseNetBackbone obj's underlying member method code with example usage: obj.download_imagenet() and returns: keras