code: def residual_block_v1(
      x, filters, kernel_size=3, stride=1, conv_shortcut=True, name=None
  ):
      """A residual block for ResNet*_v1.
  
      Args:
          x: Input tensor.
          filters: No of filters in the bottleneck layer.
          kernel_size: Kernel size of the bottleneck layer. Defaults to `3`.
          stride: Stride of the first layer. Defaults to `1`.
          conv_shortcut: Use convolution shortcut if `True`, otherwise
              use identity shortcut. Defaults to `True`
          name(optional): Name of the block
  
      Returns:
          Output tensor for the residual block.
      """
  
      if backend.image_data_format() == "channels_last":
          bn_axis = 3
      else:
          bn_axis = 1
  
      if conv_shortcut:
          shortcut = layers.Conv2D(
              4 * filters, 1, strides=stride, name=name + "_0_conv"
          )(x)
          shortcut = layers.BatchNormalization(
              axis=bn_axis, epsilon=1.001e-5, name=name + "_0_bn"
          )(shortcut)
      else:
          shortcut = x
  
      x = layers.Conv2D(
          filters, 1, strides=stride, name=name + "_1_conv"
      )(x)
      x = layers.BatchNormalization(
          axis=bn_axis, epsilon=1.001e-5, name=name + "_1_bn"
      )(x)
      x = layers.Activation("relu", name=name + "_1_relu")(x)
  
      x = layers.Conv2D(
          filters, kernel_size, padding="SAME", name=name + "_2_conv"
      )(x)
      x = layers.BatchNormalization(
          axis=bn_axis, epsilon=1.001e-5, name=name + "_2_bn"
      )(x)
      x = layers.Activation("relu", name=name + "_2_relu")(x)
  
      x = layers.Conv2D(4 * filters, 1, name=name + "_3_conv")(x)
      x = layers.BatchNormalization(
          axis=bn_axis, epsilon=1.001e-5, name=name + "_3_bn"
      )(x)
  
      x = layers.Add(name=name + "_add")([shortcut, x])
      x = layers.Activation("relu", name=name + "_out")(x)
      return x
  
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 2 for calling a global function with example usage: residual_block_v1(x, filters, kernel_size, stride, conv_shortcut, name) and returns: x