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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 general_conv(x, num_filters=64, filter_size=7, stride=1, stddev=0.02, padding="VALID", name="conv", do_norm="instance", do_relu=True, relufactor=0):
 """Generalized convolution layer."""

  with tf.variable_scope(name):
    x = layers().Conv2D(
        num_filters,
        filter_size,
        stride,
        padding,
        activation=None,
        kernel_initializer=tf.truncated_normal_initializer(stddev=stddev),
        bias_initializer=tf.constant_initializer(0.0))(x)
    if do_norm == "layer":
      x = layer_norm(x)
    elif do_norm == "instance":
      x = instance_norm(x)

    if do_relu:
      if relufactor == 0:
        x = tf.nn.relu(x, "relu")
      else:
        x = lrelu(x, leak=relufactor)

    return x