code: def MobileNetV3Large(
      input_shape=None,
      alpha=1.0,
      minimalistic=False,
      include_top=True,
      weights="imagenet",
      input_tensor=None,
      classes=1000,
      pooling=None,
      dropout_rate=0.2,
      classifier_activation="softmax",
      include_preprocessing=True,
  ):
      def stack_fn(x, kernel, activation, se_ratio):
          def depth(d):
              return _depth(d * alpha)
  
          x = _inverted_res_block(x, 1, depth(16), 3, 1, None, relu, 0)
          x = _inverted_res_block(x, 4, depth(24), 3, 2, None, relu, 1)
          x = _inverted_res_block(x, 3, depth(24), 3, 1, None, relu, 2)
          x = _inverted_res_block(
              x, 3, depth(40), kernel, 2, se_ratio, relu, 3
          )
          x = _inverted_res_block(
              x, 3, depth(40), kernel, 1, se_ratio, relu, 4
          )
          x = _inverted_res_block(
              x, 3, depth(40), kernel, 1, se_ratio, relu, 5
          )
          x = _inverted_res_block(
              x, 6, depth(80), 3, 2, None, activation, 6
          )
          x = _inverted_res_block(
              x, 2.5, depth(80), 3, 1, None, activation, 7
          )
          x = _inverted_res_block(
              x, 2.3, depth(80), 3, 1, None, activation, 8
          )
          x = _inverted_res_block(
              x, 2.3, depth(80), 3, 1, None, activation, 9
          )
          x = _inverted_res_block(
              x, 6, depth(112), 3, 1, se_ratio, activation, 10
          )
          x = _inverted_res_block(
              x, 6, depth(112), 3, 1, se_ratio, activation, 11
          )
          x = _inverted_res_block(
              x, 6, depth(160), kernel, 2, se_ratio, activation, 12
          )
          x = _inverted_res_block(
              x, 6, depth(160), kernel, 1, se_ratio, activation, 13
          )
          x = _inverted_res_block(
              x, 6, depth(160), kernel, 1, se_ratio, activation, 14
          )
          return x
  
      return MobileNetV3(
          stack_fn,
          1280,
          input_shape,
          alpha,
          "large",
          minimalistic,
          include_top,
          weights,
          input_tensor,
          classes,
          pooling,
          dropout_rate,
          classifier_activation,
          include_preprocessing,
      )
  
file_path: keras/keras/applications/mobilenet_v3.py
imports: import warnings
  from keras import backend
  from keras import layers
  from keras.api_export import keras_export
  from keras.applications import imagenet_utils
  from keras.models import Functional
  from keras.ops import operation_utils
  from keras.utils import file_utils
label_desc: Use image node_id 3 for calling a global function with example usage: MobileNetV3Large(input_shape, alpha, minimalistic, include_top, weights, input_tensor, classes, pooling, dropout_rate, classifier_activation, include_preprocessing) and returns: MobileNetV3, x, _depth