code: def main(
      cluster_mode,
      max_epoch,
      file_path,
      batch_size,
      platform,
      non_interactive,
  ):
      import matplotlib
  
      if not non_interactive and platform == "mac":
          matplotlib.use("qt5agg")
  
      if cluster_mode == "local":
          init_orca_context(cluster_mode="local", cores=4, memory="3g")
      elif cluster_mode.startswith("yarn"):
          init_orca_context(
              cluster_mode=cluster_mode,
              num_nodes=2,
              cores=2,
              driver_memory="3g",
          )
      elif cluster_mode == "spark-submit":
          init_orca_context(cluster_mode="spark-submit")
      load_data(file_path)
      img_dir = os.path.join(file_path, "train")
      label_dir = os.path.join(file_path, "train_masks")
  
      # Here we only take the first 1000 files for simplicity
      is_local_and_existing_uri(
          os.path.join(file_path, "train_masks.csv")
      )
      df_train = pd.read_csv(os.path.join(file_path, "train_masks.csv"))
      ids_train = df_train["img"].map(lambda s: s.split(".")[0])
      ids_train = ids_train[:1000]
  
      x_train_filenames = []
      y_train_filenames = []
      for img_id in ids_train:
          x_train_filenames.append(
              os.path.join(img_dir, "{}.jpg".format(img_id))
          )
          y_train_filenames.append(
              os.path.join(label_dir, "{}_mask.gif".format(img_id))
          )
  
      (
          x_train_filenames,
          x_val_filenames,
          y_train_filenames,
          y_val_filenames,
      ) = train_test_split(
          x_train_filenames,
          y_train_filenames,
          test_size=0.2,
          random_state=42,
      )
  
      def load_and_process_image(path):
          array = mpimg.imread(path)
          result = np.array(
              Image.fromarray(array).resize(size=(128, 128))
          )
          result = result.astype(float)
          result /= 255.0
          return result
  
      def load_and_process_image_label(path):
          array = mpimg.imread(path)
          result = np.array(
              Image.fromarray(array).resize(size=(128, 128))
          )
          result = np.expand_dims(result[:, :, 1], axis=-1)
          result = result.astype(float)
          result /= 255.0
          return result
  
      train_images = np.stack(
          [
              load_and_process_image(filepath)
              for filepath in x_train_filenames
          ]
      )
      train_label_images = np.stack(
          [
              load_and_process_image_label(filepath)
              for filepath in y_train_filenames
          ]
      )
      val_images = np.stack(
          [
              load_and_process_image(filepath)
              for filepath in x_val_filenames
          ]
      )
      val_label_images = np.stack(
          [
              load_and_process_image_label(filepath)
              for filepath in y_val_filenames
          ]
      )
      train_shards = XShards.partition(
          {"x": train_images, "y": train_label_images}
      )
      val_shards = XShards.partition(
          {"x": val_images, "y": val_label_images}
      )
  
      # Build the U-Net model
      def conv_block(input_tensor, num_filters):
          encoder = layers.Conv2D(num_filters, (3, 3), padding="same")(
              input_tensor
          )
          encoder = layers.Activation("relu")(encoder)
          encoder = layers.Conv2D(num_filters, (3, 3), padding="same")(
              encoder
          )
          encoder = layers.Activation("relu")(encoder)
          return encoder
  
      def encoder_block(input_tensor, num_filters):
          encoder = conv_block(input_tensor, num_filters)
          encoder_pool = layers.MaxPooling2D((2, 2), strides=(2, 2))(
              encoder
          )
  
          return encoder_pool, encoder
  
      def decoder_block(input_tensor, concat_tensor, num_filters):
          decoder = layers.Conv2DTranspose(
              num_filters, (2, 2), strides=(2, 2), padding="same"
          )(input_tensor)
          decoder = layers.concatenate(
              [concat_tensor, decoder], axis=-1
          )
          decoder = layers.Activation("relu")(decoder)
          decoder = layers.Conv2D(num_filters, (3, 3), padding="same")(
              decoder
          )
          decoder = layers.Activation("relu")(decoder)
          decoder = layers.Conv2D(num_filters, (3, 3), padding="same")(
              decoder
          )
          decoder = layers.Activation("relu")(decoder)
          return decoder
  
      inputs = layers.Input(shape=(128, 128, 3))  # 128
      encoder0_pool, encoder0 = encoder_block(inputs, 16)  # 64
      encoder1_pool, encoder1 = encoder_block(encoder0_pool, 32)  # 32
      encoder2_pool, encoder2 = encoder_block(encoder1_pool, 64)  # 16
      encoder3_pool, encoder3 = encoder_block(encoder2_pool, 128)  # 8
      center = conv_block(encoder3_pool, 256)  # center
      decoder3 = decoder_block(center, encoder3, 128)  # 16
      decoder2 = decoder_block(decoder3, encoder2, 64)  # 32
      decoder1 = decoder_block(decoder2, encoder1, 32)  # 64
      decoder0 = decoder_block(decoder1, encoder0, 16)  # 128
      outputs = layers.Conv2D(1, (1, 1), activation="sigmoid")(decoder0)
  
      net = models.Model(inputs=[inputs], outputs=[outputs])
  
      # Define custom metrics
      def dice_coeff(y_true, y_pred):
          smooth = 1.0
          # Flatten
          y_true_f = tf.reshape(y_true, [-1])
          y_pred_f = tf.reshape(y_pred, [-1])
          intersection = tf.reduce_sum(y_true_f * y_pred_f)
          score = (2.0 * intersection + smooth) / (
              tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth
          )
          return score
  
      # Define custom loss function
      def dice_loss(y_true, y_pred):
          loss = 1 - dice_coeff(y_true, y_pred)
          return loss
  
      def bce_dice_loss(y_true, y_pred):
          loss = losses.binary_crossentropy(y_true, y_pred) + dice_loss(
              y_true, y_pred
          )
          return loss
  
      # compile model
      net.compile(
          optimizer=tf.keras.optimizers.Adam(2e-3), loss=bce_dice_loss
      )
      print(net.summary())
  
      # create an estimator from keras model
      est = Estimator.from_keras(keras_model=net)
      # fit with estimator
      est.fit(
          data=train_shards, batch_size=batch_size, epochs=max_epoch
      )
      # evaluate with estimator
      result = est.evaluate(val_shards)
      print(result)
      # predict with estimator
      val_shards.cache()
      val_image_shards = val_shards.transform_shard(
          lambda val_dict: {"x": val_dict["x"]}
      )
      pred_shards = est.predict(
          data=val_image_shards, batch_size=batch_size
      )
      pred = pred_shards.collect()[0]["prediction"]
      val_image_label = val_shards.collect()[0]
      val_image = val_image_label["x"]
      val_label = val_image_label["y"]
      if not non_interactive:
          # visualize 5 predicted results
          plt.figure(figsize=(10, 20))
          for i in range(5):
              img = val_image[i]
              label = val_label[i]
              predicted_label = pred[i]
  
              plt.subplot(5, 3, 3 * i + 1)
              plt.imshow(img)
              plt.title("Input image")
  
              plt.subplot(5, 3, 3 * i + 2)
              plt.imshow(label[:, :, 0], cmap="gray")
              plt.title("Actual Mask")
              plt.subplot(5, 3, 3 * i + 3)
              plt.imshow(predicted_label, cmap="gray")
              plt.title("Predicted Mask")
          plt.suptitle("Examples of Input Image, Label, and Prediction")
  
          plt.show()
  
      stop_orca_context()
  
imports: import argparse
  import os
  import zipfile
  import pandas
  from PIL import Image
  import tensorflow
  from tensorflow.python.keras import layers
  from tensorflow.python.keras import losses
  from tensorflow.python.keras import models
  from tensorflow.python.keras import backend
  import matplotlib.image
  import matplotlib.pyplot
  from sklearn.model_selection import train_test_split
  import numpy
  from bigdl.orca import init_orca_context, stop_orca_context
  from bigdl.orca.data import XShards
  from bigdl.orca.learn.tf.estimator import Estimator
  from urllib.parse import urlparse
  from os.path import exists
  from bigdl.dllib.utils import log4Error
label_desc: Use image node_id 4 for calling a global function with example usage: main(cluster_mode, max_epoch, file_path, batch_size, platform, non_interactive) and returns: result, result, encoder, encoder_pool,  encoder, decoder, score, loss, loss