code: def MobileNet(
      input_shape=None,
      alpha=1.0,
      depth_multiplier=1,
      dropout=1e-3,
      include_top=True,
      weights="imagenet",
      input_tensor=None,
      pooling=None,
      classes=1000,
      classifier_activation="softmax",
  ):
      """Instantiates the MobileNet architecture.
  
      Reference:
      - [MobileNets: Efficient Convolutional Neural Networks
         for Mobile Vision Applications](
          https://arxiv.org/abs/1704.04861)
  
      This function returns a Keras image classification model,
      optionally loaded with weights pre-trained on ImageNet.
  
      For image classification use cases, see
      [this page for detailed examples](
      https://keras.io/api/applications/#usage-examples-for-image-classification-models).
  
      For transfer learning use cases, make sure to read the
      [guide to transfer learning & fine-tuning](
      https://keras.io/guides/transfer_learning/).
  
      Note: each Keras Application expects a specific kind of input preprocessing.
      For MobileNet, call `keras.applications.mobilenet.preprocess_input`
      on your inputs before passing them to the model.
      `mobilenet.preprocess_input` will scale input pixels between -1 and 1.
  
      Args:
          input_shape: Optional shape tuple, only to be specified if `include_top`
              is `False` (otherwise the input shape has to be `(224, 224, 3)`
              (with `"channels_last"` data format) or `(3, 224, 224)`
              (with `"channels_first"` data format).
              It should have exactly 3 inputs channels, and width and
              height should be no smaller than 32. E.g. `(200, 200, 3)` would
              be one valid value. Defaults to `None`.
              `input_shape` will be ignored if the `input_tensor` is provided.
          alpha: Controls the width of the network. This is known as the width
              multiplier in the MobileNet paper.
              - If `alpha < 1.0`, proportionally decreases the number
                  of filters in each layer.
              - If `alpha > 1.0`, proportionally increases the number
                  of filters in each layer.
              - If `alpha == 1`, default number of filters from the paper
                  are used at each layer. Defaults to `1.0`.
          depth_multiplier: Depth multiplier for depthwise convolution.
              This is called the resolution multiplier in the MobileNet paper.
              Defaults to `1.0`.
          dropout: Dropout rate. Defaults to `0.001`.
          include_top: Boolean, whether to include the fully-connected layer
              at the top of the network. Defaults to `True`.
          weights: One of `None` (random initialization), `"imagenet"`
              (pre-training on ImageNet), or the path to the weights file
              to be loaded. Defaults to `"imagenet"`.
          input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`)
              to use as image input for the model. `input_tensor` is useful
              for sharing inputs between multiple different networks.
              Defaults to `None`.
          pooling: Optional pooling mode for feature extraction when `include_top`
              is `False`.
              - `None` (default) means that the output of the model will be
                  the 4D tensor output of the last convolutional block.
              - `avg` means that global average pooling
                  will be applied to the output of the
                  last convolutional block, and thus
                  the output of the model will be a 2D tensor.
              - `max` means that global max pooling will be applied.
          classes: Optional number of classes to classify images into,
              only to be specified if `include_top` is `True`, and if
              no `weights` argument is specified. Defaults to `1000`.
          classifier_activation: A `str` or callable. The activation function
              to use on the "top" layer. Ignored unless `include_top=True`.
              Set `classifier_activation=None` to return the logits of the "top"
              layer. When loading pretrained weights, `classifier_activation`
              can only be `None` or `"softmax"`.
  
      Returns:
          A model instance.
      """
      if not (
          weights in {"imagenet", None} or file_utils.exists(weights)
      ):
          raise ValueError(
              "The `weights` argument should be either "
              "`None` (random initialization), 'imagenet' "
              "(pre-training on ImageNet), "
              "or the path to the weights file to be loaded. "
              f"Received weights={weights}"
          )
  
      if weights == "imagenet" and include_top and classes != 1000:
          raise ValueError(
              "If using `weights='imagenet'` with `include_top=True`, "
              "`classes` should be 1000.  "
              f"Received classes={classes}"
          )
  
      # Determine proper input shape and default size.
      if input_shape is None:
          default_size = 224
      else:
          if backend.image_data_format() == "channels_first":
              rows = input_shape[1]
              cols = input_shape[2]
          else:
              rows = input_shape[0]
              cols = input_shape[1]
  
          if rows == cols and rows in [128, 160, 192, 224]:
              default_size = rows
          else:
              default_size = 224
  
      input_shape = imagenet_utils.obtain_input_shape(
          input_shape,
          default_size=default_size,
          min_size=32,
          data_format=backend.image_data_format(),
          require_flatten=include_top,
          weights=weights,
      )
  
      if backend.image_data_format() == "channels_last":
          row_axis, col_axis = (0, 1)
      else:
          row_axis, col_axis = (1, 2)
      rows = input_shape[row_axis]
      cols = input_shape[col_axis]
  
      if weights == "imagenet":
          if depth_multiplier != 1:
              raise ValueError(
                  "If imagenet weights are being loaded, "
                  "depth multiplier must be 1.  "
                  f"Received depth_multiplier={depth_multiplier}"
              )
  
          if alpha not in [0.25, 0.50, 0.75, 1.0]:
              raise ValueError(
                  "If imagenet weights are being loaded, "
                  "alpha can be one of"
                  "`0.25`, `0.50`, `0.75` or `1.0` only.  "
                  f"Received alpha={alpha}"
              )
  
          if rows != cols or rows not in [128, 160, 192, 224]:
              rows = 224
              warnings.warn(
                  "`input_shape` is undefined or non-square, "
                  "or `rows` is not in [128, 160, 192, 224]. "
                  "Weights for input shape (224, 224) will be "
                  "loaded as the default.",
                  stacklevel=2,
              )
  
      if input_tensor is None:
          img_input = layers.Input(shape=input_shape)
      else:
          if not backend.is_keras_tensor(input_tensor):
              img_input = layers.Input(
                  tensor=input_tensor, shape=input_shape
              )
          else:
              img_input = input_tensor
  
      x = _conv_block(img_input, 32, alpha, strides=(2, 2))
      x = _depthwise_conv_block(
          x, 64, alpha, depth_multiplier, block_id=1
      )
  
      x = _depthwise_conv_block(
          x, 128, alpha, depth_multiplier, strides=(2, 2), block_id=2
      )
      x = _depthwise_conv_block(
          x, 128, alpha, depth_multiplier, block_id=3
      )
  
      x = _depthwise_conv_block(
          x, 256, alpha, depth_multiplier, strides=(2, 2), block_id=4
      )
      x = _depthwise_conv_block(
          x, 256, alpha, depth_multiplier, block_id=5
      )
  
      x = _depthwise_conv_block(
          x, 512, alpha, depth_multiplier, strides=(2, 2), block_id=6
      )
      x = _depthwise_conv_block(
          x, 512, alpha, depth_multiplier, block_id=7
      )
      x = _depthwise_conv_block(
          x, 512, alpha, depth_multiplier, block_id=8
      )
      x = _depthwise_conv_block(
          x, 512, alpha, depth_multiplier, block_id=9
      )
      x = _depthwise_conv_block(
          x, 512, alpha, depth_multiplier, block_id=10
      )
      x = _depthwise_conv_block(
          x, 512, alpha, depth_multiplier, block_id=11
      )
  
      x = _depthwise_conv_block(
          x, 1024, alpha, depth_multiplier, strides=(2, 2), block_id=12
      )
      x = _depthwise_conv_block(
          x, 1024, alpha, depth_multiplier, block_id=13
      )
  
      if include_top:
          x = layers.GlobalAveragePooling2D(keepdims=True)(x)
          x = layers.Dropout(dropout, name="dropout")(x)
          x = layers.Conv2D(
              classes, (1, 1), padding="same", name="conv_preds"
          )(x)
          x = layers.Reshape((classes,), name="reshape_2")(x)
          imagenet_utils.validate_activation(
              classifier_activation, weights
          )
          x = layers.Activation(
              activation=classifier_activation, name="predictions"
          )(x)
      else:
          if pooling == "avg":
              x = layers.GlobalAveragePooling2D()(x)
          elif pooling == "max":
              x = layers.GlobalMaxPooling2D()(x)
  
      # Ensure that the model takes into account
      # any potential predecessors of `input_tensor`.
      if input_tensor is not None:
          inputs = operation_utils.get_source_inputs(input_tensor)
      else:
          inputs = img_input
  
      # Create model.
      model = Functional(
          inputs, x, name=f"mobilenet_{alpha:0.2f}_{rows}"
      )
  
      # Load weights.
      if weights == "imagenet":
          if alpha == 1.0:
              alpha_text = "1_0"
          elif alpha == 0.75:
              alpha_text = "7_5"
          elif alpha == 0.50:
              alpha_text = "5_0"
          else:
              alpha_text = "2_5"
  
          if include_top:
              model_name = "mobilenet_%s_%d_tf.h5" % (alpha_text, rows)
              weight_path = BASE_WEIGHT_PATH + model_name
              weights_path = file_utils.get_file(
                  model_name, weight_path, cache_subdir="models"
              )
          else:
              model_name = "mobilenet_%s_%d_tf_no_top.h5" % (
                  alpha_text,
                  rows,
              )
              weight_path = BASE_WEIGHT_PATH + model_name
              weights_path = file_utils.get_file(
                  model_name, weight_path, cache_subdir="models"
              )
          model.load_weights(weights_path)
      elif weights is not None:
          model.load_weights(weights)
  
      return model
  
file_path: keras/keras/applications/mobilenet.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 1 for calling a global function with example usage: MobileNet(input_shape, alpha, depth_multiplier, dropout, include_top, weights, input_tensor, pooling, classes, classifier_activation) and returns: model