code: def ResNet(
      stack_fn,
      preact,
      use_bias,
      model_name="resnet",
      include_top=True,
      weights="imagenet",
      input_tensor=None,
      input_shape=None,
      pooling=None,
      classes=1000,
      classifier_activation="softmax",
  ):
      """Instantiates the ResNet, ResNetV2, and ResNeXt architecture.
  
      Args:
          stack_fn: A function that returns output tensor for the
              stacked residual blocks.
          preact: Whether to use pre-activation or not. `True` for ResNetV2,
              `False` for ResNet and ResNeXt.
          use_bias: Whether to use biases for convolutional layers or not.
              `True` for ResNet and ResNetV2, `False` for ResNeXt.
          model_name: Name of the model.
          include_top: Whether to include the fully-connected
              layer at the top of the network.
          weights: One of `None` (random initialization),
              `"imagenet"` (pre-training on ImageNet),
              or the path to the weights file to be loaded.
          input_tensor: Optional Keras tensor (i.e. output of `layers.Input()`)
              to use as image input for the model.
          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.
          pooling: Optional pooling mode for feature extraction
              when `include_top` is `False`.
              - `None` means that the output of the model will be
                  the 4D tensor output of the
                  last convolutional layer.
              - `avg` means that global average pooling
                  will be applied to the output of the
                  last convolutional layer, 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.
          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.  Received: "
              f"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
      input_shape = imagenet_utils.obtain_input_shape(
          input_shape,
          default_size=224,
          min_size=32,
          data_format=backend.image_data_format(),
          require_flatten=include_top,
          weights=weights,
      )
  
      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
  
      if backend.image_data_format() == "channels_last":
          bn_axis = 3
      else:
          bn_axis = 1
  
      x = layers.ZeroPadding2D(
          padding=((3, 3), (3, 3)), name="conv1_pad"
      )(img_input)
      x = layers.Conv2D(
          64, 7, strides=2, use_bias=use_bias, name="conv1_conv"
      )(x)
  
      if not preact:
          x = layers.BatchNormalization(
              axis=bn_axis, epsilon=1.001e-5, name="conv1_bn"
          )(x)
          x = layers.Activation("relu", name="conv1_relu")(x)
  
      x = layers.ZeroPadding2D(
          padding=((1, 1), (1, 1)), name="pool1_pad"
      )(x)
      x = layers.MaxPooling2D(3, strides=2, name="pool1_pool")(x)
  
      x = stack_fn(x)
  
      if preact:
          x = layers.BatchNormalization(
              axis=bn_axis, epsilon=1.001e-5, name="post_bn"
          )(x)
          x = layers.Activation("relu", name="post_relu")(x)
  
      if include_top:
          x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
  
          # Validate activation for the classifier layer
          imagenet_utils.validate_activation(
              classifier_activation, weights
          )
  
          x = layers.Dense(
              classes,
              activation=classifier_activation,
              name="predictions",
          )(x)
      else:
          if pooling == "avg":
              x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
          elif pooling == "max":
              x = layers.GlobalMaxPooling2D(name="max_pool")(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=model_name)
  
      # Load weights.
      if (weights == "imagenet") and (model_name in WEIGHTS_HASHES):
          if include_top:
              file_name = (
                  model_name + "_weights_tf_dim_ordering_tf_kernels.h5"
              )
              file_hash = WEIGHTS_HASHES[model_name][0]
          else:
              file_name = (
                  model_name
                  + "_weights_tf_dim_ordering_tf_kernels_notop.h5"
              )
              file_hash = WEIGHTS_HASHES[model_name][1]
          weights_path = file_utils.get_file(
              file_name,
              BASE_WEIGHTS_PATH + file_name,
              cache_subdir="models",
              file_hash=file_hash,
          )
          model.load_weights(weights_path)
      elif weights is not None:
          model.load_weights(weights)
  
      return model
  
file_path: keras/keras/applications/resnet.py
imports: 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: ResNet(stack_fn, preact, use_bias, model_name, include_top, weights, input_tensor, input_shape, pooling, classes, classifier_activation) and returns: model