code: def _build_model(self, input_shape):
      x = Input(shape=(32, 32, 3))
      y = x
      y = Convolution2D(
          filters=64,
          kernel_size=3,
          strides=1,
          padding="same",
          activation="relu",
          kernel_initializer="he_normal",
      )(y)
      y = Convolution2D(
          filters=64,
          kernel_size=3,
          strides=1,
          padding="same",
          activation="relu",
          kernel_initializer="he_normal",
      )(y)
      y = MaxPooling2D(pool_size=2, strides=2, padding="same")(y)
  
      y = Convolution2D(
          filters=128,
          kernel_size=3,
          strides=1,
          padding="same",
          activation="relu",
          kernel_initializer="he_normal",
      )(y)
      y = Convolution2D(
          filters=128,
          kernel_size=3,
          strides=1,
          padding="same",
          activation="relu",
          kernel_initializer="he_normal",
      )(y)
      y = MaxPooling2D(pool_size=2, strides=2, padding="same")(y)
  
      y = Convolution2D(
          filters=256,
          kernel_size=3,
          strides=1,
          padding="same",
          activation="relu",
          kernel_initializer="he_normal",
      )(y)
      y = Convolution2D(
          filters=256,
          kernel_size=3,
          strides=1,
          padding="same",
          activation="relu",
          kernel_initializer="he_normal",
      )(y)
      y = MaxPooling2D(pool_size=2, strides=2, padding="same")(y)
  
      y = Flatten()(y)
      y = Dropout(self.config.get("dropout", 0.5))(y)
      y = Dense(
          units=10, activation="softmax", kernel_initializer="he_normal"
      )(y)
  
      model = Model(inputs=x, outputs=y, name="model1")
      return model
  
imports: from __future__ import print_function
  import argparse
  import numpy
  import tensorflow
  from tensorflow.keras.datasets import cifar10
  from tensorflow.keras.layers import Input, Dense, Dropout, Flatten
  from tensorflow.keras.layers import Convolution2D, MaxPooling2D
  from tensorflow.keras.models import Model, load_model
  from tensorflow.keras.preprocessing.image import ImageDataGenerator
  from ray import train, tune
  from ray.tune import Trainable
  from ray.tune.schedulers import PopulationBasedTraining
label_desc: Use image node_id 2 for calling the Cifar10Model obj's underlying member method code with example usage: obj._build_model(input_shape) and returns: model