code: def block1(
      x, filters, kernel_size=3, stride=1, conv_shortcut=True, name=None
  ):
      """A residual block.
  
      Args:
          x: input tensor.
          filters: integer, filters of the bottleneck layer.
          kernel_size: default 3, kernel size of the bottleneck layer.
          stride: default 1, stride of the first layer.
          conv_shortcut: default True, use convolution shortcut if True,
              otherwise identity shortcut.
          name: string, block label.
  
      Returns:
          Output tensor for the residual block.
      """
      bn_axis = (
          3
          if keras.backend.image_data_format() == "channels_last"
          else 1
      )
  
      if conv_shortcut is True:
          shortcut = layers.Conv2D(
              4 * filters, 1, strides=stride, name=f"{name}_0_conv"
          )(x)
  
          shortcut = layers.BatchNormalization(
              axis=bn_axis, epsilon=1.001e-5, name=f"{name}_0_bn"
          )(shortcut)
  
      else:
          shortcut = x
  
      x = layers.Conv2D(
          filters, 1, strides=stride, name=f"{name}_1_conv"
      )(x)
      x = layers.BatchNormalization(
          axis=bn_axis, epsilon=1.001e-5, name=f"{name}_1_bn"
      )(x)
  
      x = layers.Activation("relu", name=f"{name}_1_relu")(x)
  
      x = layers.Conv2D(
          filters, kernel_size, padding="same", name=f"{name}_2_conv"
      )(x)
  
      x = layers.BatchNormalization(
          axis=bn_axis, epsilon=1.001e-5, name=f"{name}_2_bn"
      )(x)
  
      x = layers.Activation("relu", name=f"{name}_2_relu")(x)
  
      x = layers.Conv2D(4 * filters, 1, name=f"{name}_3_conv")(x)
      x = layers.BatchNormalization(
          axis=bn_axis, epsilon=1.001e-5, name=f"{name}_3_bn"
      )(x)
  
      x = layers.Add(name=f"{name}_add")([shortcut, x])
      x = layers.Activation("relu", name=f"{name}_out")(x)
      return x
  
file_path: keras-tuner/keras_tuner/applications/resnet.py
imports: from keras_tuner.api_export import keras_tuner_export
  from keras_tuner.backend import keras
  from keras_tuner.backend import ops
  from keras_tuner.backend.keras import layers
  from keras_tuner.engine import hypermodel
label_desc: Use image node_id 1 for calling a global function with example usage: block1(x, filters, kernel_size, stride, conv_shortcut, name) and returns: x