code: def _se_block(inputs, filters, se_ratio, prefix):
      x = layers.GlobalAveragePooling2D(
          keepdims=True, name=prefix + "squeeze_excite_avg_pool"
      )(inputs)
      x = layers.Conv2D(
          _depth(filters * se_ratio),
          kernel_size=1,
          padding="same",
          name=prefix + "squeeze_excite_conv",
      )(x)
      x = layers.ReLU(name=prefix + "squeeze_excite_relu")(x)
      x = layers.Conv2D(
          filters,
          kernel_size=1,
          padding="same",
          name=prefix + "squeeze_excite_conv_1",
      )(x)
      x = hard_sigmoid(x)
      x = layers.Multiply(name=prefix + "squeeze_excite_mul")(
          [inputs, x]
      )
      return x
  
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 8 for calling a global function with example usage: _se_block(inputs, filters, se_ratio, prefix) and returns: x