class_docstr: A pre-trained image classifier model.
  
  :param model_path The path containing the pre-trained model
class_docstr_tok: ["A","pre-trained","image","classifier","model",".",":","param","model_path","The","path","containing","the","pre-trained","model"]
docstr: Load an existing object detection model (with weights).
  
  # Arguments
  path: The path to save the model. Local file system, HDFS and Amazon S3 are supported.
        HDFS path should be like 'hdfs://[host]:[port]/xxx'.
        Amazon S3 path should be like 's3a://bucket/xxx'.
docstr_tok: ["Load","an","existing","object","detection","model","(","with","weights",")",".","#","Arguments","path",":","The","path","to","save","the","model",".","Local","file","system",",","HDFS","and","Amazon","S3","are","supported",".","HDFS","path","should","be","like","'hdfs",":","\/\/","[","host","]",":","[","port","]","\/xxx","'",".","Amazon","S3","path","should","be","like","'s3a",":","\/\/bucket\/xxx","'","."]
code: def load_model(path, weight_path=None, bigdl_type="float"):
      """
      Load an existing object detection model (with weights).
  
      # Arguments
      path: The path to save the model. Local file system, HDFS and Amazon S3 are supported.
            HDFS path should be like 'hdfs://[host]:[port]/xxx'.
            Amazon S3 path should be like 's3a://bucket/xxx'.
      """
      jmodel = callZooFunc(
          bigdl_type, "loadImageClassifier", path, weight_path
      )
      model = ImageModel._do_load(jmodel, bigdl_type)
      model.__class__ = ImageClassifier
      return model
  
file_path: analytics-zoo/pyzoo/zoo/models/image/imageclassification/image_classification.py
imports: from bigdl.transform.vision.image import FeatureTransformer
  from zoo.models.image.common.image_model import ImageModel
  from zoo.feature.image.imageset import *
  from zoo.common.utils import callZooFunc
label_desc: Use image node_id 2 for calling the ImageClassifier obj's underlying member method code with example usage: obj.load_model(path, weight_path, bigdl_type) and returns: model