class_docstr: Convolution layers with residual connection
docstr: Initialize parameters.
  
  Args:
      conv_dims (list): List of the dimensions of the Feedforward layer.
      dropout_rate (float): Dropout probability.
docstr_tok: ["Initialize","parameters",".","Args",":","conv_dims","(","list",")",":","List","of","the","dimensions","of","the","Feedforward","layer",".","dropout_rate","(","float",")",":","Dropout","probability","."]
code: def __init__(self, conv_dims, dropout_rate):
      """Initialize parameters.
  
      Args:
          conv_dims (list): List of the dimensions of the Feedforward layer.
          dropout_rate (float): Dropout probability.
      """
      super(PointWiseFeedForward, self).__init__()
      self.conv_dims = conv_dims
      self.dropout_rate = dropout_rate
      self.conv_layer1 = tf.keras.layers.Conv1D(
          filters=self.conv_dims[0],
          kernel_size=1,
          activation="relu",
          use_bias=True,
      )
      self.conv_layer2 = tf.keras.layers.Conv1D(
          filters=self.conv_dims[1],
          kernel_size=1,
          activation=None,
          use_bias=True,
      )
      self.dropout_layer = tf.keras.layers.Dropout(self.dropout_rate)
  
imports: import random
  import numpy
  from tqdm import tqdm
  import tensorflow
  from recommenders.utils.timer import Timer
label_desc: Use image node_id 1 to create a new PointWiseFeedForward object from inherited base classes: tf.keras.layers with example: obj = PointWiseFeedForward(conv_dims, dropout_rate)