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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def conv_layer(x, hidden_size, kernel_size, stride, pooling_window, dropout_rate, dilation_rate, name="conv"):
 """Single conv layer with relu, optional pooling, and dropout."""

  with tf.variable_scope(name):
    out = x
    out = common_layers.conv1d_block(
        out,
        hidden_size, [(dilation_rate, kernel_size)],
        strides=stride,
        first_relu=False,
        padding="same")
    out = tf.nn.relu(out)
    if pooling_window:
      out = tf.layers.max_pooling1d(
          out, pooling_window, pooling_window, padding="same")
    out = tf.layers.dropout(out, dropout_rate)
    return out