code: def loadModel(
      url="https://github.com/serengil/deepface_models/releases/download/v1.0/facial_expression_model_weights.h5",
  ) -> Sequential:
      num_classes = 7
  
      model = Sequential()
  
      # 1st convolution layer
      model.add(
          Conv2D(64, (5, 5), activation="relu", input_shape=(48, 48, 1))
      )
      model.add(MaxPooling2D(pool_size=(5, 5), strides=(2, 2)))
  
      # 2nd convolution layer
      model.add(Conv2D(64, (3, 3), activation="relu"))
      model.add(Conv2D(64, (3, 3), activation="relu"))
      model.add(AveragePooling2D(pool_size=(3, 3), strides=(2, 2)))
  
      # 3rd convolution layer
      model.add(Conv2D(128, (3, 3), activation="relu"))
      model.add(Conv2D(128, (3, 3), activation="relu"))
      model.add(AveragePooling2D(pool_size=(3, 3), strides=(2, 2)))
  
      model.add(Flatten())
  
      # fully connected neural networks
      model.add(Dense(1024, activation="relu"))
      model.add(Dropout(0.2))
      model.add(Dense(1024, activation="relu"))
      model.add(Dropout(0.2))
  
      model.add(Dense(num_classes, activation="softmax"))
  
      # ----------------------------
  
      home = functions.get_deepface_home()
  
      if (
          os.path.isfile(
              home
              + "/.deepface/weights/facial_expression_model_weights.h5"
          )
          != True
      ):
          logger.info(
              "facial_expression_model_weights.h5 will be downloaded..."
          )
  
          output = (
              home
              + "/.deepface/weights/facial_expression_model_weights.h5"
          )
          gdown.download(url, output, quiet=False)
  
      model.load_weights(
          home + "/.deepface/weights/facial_expression_model_weights.h5"
      )
  
      return model
  
imports: import os
  import gdown
  import tensorflow
  from deepface.commons import functions
  from deepface.commons.logger import Logger
label_desc: Use image node_id 1 for calling a global function with example usage: loadModel(url) and returns: model