code: def __init__(
      self,
      max_text_length,
      filters=64,
      kernel_sizes=[3],
      dropout_rate=0.5,
      name="",
      **kwargs,
  ):
      super(TextProcessor, self).__init__(name=name, **kwargs)
      self.max_text_length = max_text_length
      self.filters = filters
      self.kernel_sizes = kernel_sizes
      self.conv = []
      self.maxpool = []
      for kernel_size in kernel_sizes:
          self.conv.append(
              layers.Conv2D(
                  self.filters,
                  kernel_size=(1, kernel_size),
                  use_bias=True,
                  activation="relu",
              )
          )
          self.maxpool.append(
              layers.MaxPooling2D(
                  pool_size=(1, self.max_text_length - kernel_size + 1)
              )
          )
      self.reshape = layers.Reshape(
          target_shape=(-1, self.filters * len(kernel_sizes))
      )
      self.dropout_rate = dropout_rate
      self.dropout = layers.Dropout(rate=self.dropout_rate)
  
imports: import numpy
  import tensorflow
  from tensorflow import keras
  from tensorflow.keras import layers, initializers, Input
  from tensorflow.python.keras.preprocessing.sequence import pad_sequences
  from ...utils import get_rng
  from ...utils.init_utils import uniform
label_desc: Use image node_id 1 to create a new TextProcessor object from inherited base classes: keras with example: obj = TextProcessor(max_text_length, filters, kernel_sizes, dropout_rate, name)