diff --git a/manim_ml/list_group.py b/manim_ml/list_group.py
index 2dff075..f08a3d3 100644
--- a/manim_ml/list_group.py
+++ b/manim_ml/list_group.py
@@ -67,7 +67,7 @@ class ListGroup(Mobject):
         """Length of items"""
         return len(self.items)
 
-    def set_z_index(self, z_index_value):
+    def set_z_index(self, z_index_value, family=True):
         """Sets z index of all values in ListGroup"""
         for item in self.items:
-            item.set_z_index(z_index_value)
\ No newline at end of file
+            item.set_z_index(z_index_value, family=True)
\ No newline at end of file
diff --git a/manim_ml/neural_network/layers/convolutional.py b/manim_ml/neural_network/layers/convolutional.py
new file mode 100644
index 0000000..7434099
--- /dev/null
+++ b/manim_ml/neural_network/layers/convolutional.py
@@ -0,0 +1,25 @@
+
+from manim import *
+from manim_ml.neural_network.layers.parent_layers import VGroupNeuralNetworkLayer
+
+class ConvolutionalLayer(VGroupNeuralNetworkLayer):
+    """Handles rendering a convolutional layer for a nn"""
+
+    def __init__(self, num_filters, filter_width, **kwargs):
+        super(VGroupNeuralNetworkLayer, self).__init__(**kwargs)
+        self.num_filters = num_filters
+        self.filter_width = filter_width
+
+        self._construct_neural_network_layer()
+
+    def _construct_neural_network_layer(self):
+        """Creates the neural network layer"""
+        pass
+
+    def make_forward_pass_animation(self):
+        # make highlight animation
+        return None
+
+    @override_animation(Create)
+    def _create_override(self, **kwargs):
+        pass
diff --git a/manim_ml/neural_network/layers/convolutional_to_convolutional.py b/manim_ml/neural_network/layers/convolutional_to_convolutional.py
new file mode 100644
index 0000000..e69de29
diff --git a/manim_ml/neural_network/layers/embedding.py b/manim_ml/neural_network/layers/embedding.py
index 86d0b00..d3c7ea1 100644
--- a/manim_ml/neural_network/layers/embedding.py
+++ b/manim_ml/neural_network/layers/embedding.py
@@ -6,7 +6,7 @@ class EmbeddingLayer(VGroupNeuralNetworkLayer):
     """NeuralNetwork embedding object that can show probability distributions"""
 
     def __init__(self, point_radius=0.02, **kwargs):
-        super(EmbeddingLayer, self).__init__(**kwargs)
+        super(VGroupNeuralNetworkLayer, self).__init__(**kwargs)
         self.point_radius = point_radius
         self.axes = Axes(
             tips=False,
diff --git a/manim_ml/neural_network/layers/feed_forward_to_feed_forward.py b/manim_ml/neural_network/layers/feed_forward_to_feed_forward.py
index bc000f0..43791f0 100644
--- a/manim_ml/neural_network/layers/feed_forward_to_feed_forward.py
+++ b/manim_ml/neural_network/layers/feed_forward_to_feed_forward.py
@@ -11,7 +11,7 @@ class FeedForwardToFeedForward(ConnectiveLayer):
                 dot_radius=0.05, animation_dot_color=RED, edge_color=WHITE,
                 edge_width=1.5, **kwargs):
         super().__init__(input_layer, output_layer, input_class=FeedForwardLayer, output_class=FeedForwardLayer,
-                            **kwargs)
+                        **kwargs)
         self.passing_flash = passing_flash
         self.edge_color = edge_color
         self.dot_radius = dot_radius
diff --git a/manim_ml/neural_network/layers/feed_forward_to_image.py b/manim_ml/neural_network/layers/feed_forward_to_image.py
index 5dfe07c..847a1b4 100644
--- a/manim_ml/neural_network/layers/feed_forward_to_image.py
+++ b/manim_ml/neural_network/layers/feed_forward_to_image.py
@@ -10,7 +10,7 @@ class FeedForwardToImage(ConnectiveLayer):
 
     def __init__(self, input_layer, output_layer, animation_dot_color=RED,
                 dot_radius=0.05, **kwargs):
-        super().__init__(input_layer, output_layer, input_class=FeedForwardLayer, output_class=ImageLayer
+        super().__init__(input_layer, output_layer, input_class=FeedForwardLayer, output_class=ImageLayer,
                         **kwargs)
         self.animation_dot_color = animation_dot_color
         self.dot_radius = dot_radius
diff --git a/manim_ml/neural_network/layers/image.py b/manim_ml/neural_network/layers/image.py
index 5401b61..e3081f9 100644
--- a/manim_ml/neural_network/layers/image.py
+++ b/manim_ml/neural_network/layers/image.py
@@ -13,20 +13,23 @@ class ImageLayer(NeuralNetworkLayer):
             self.image_mobject = GrayscaleImageMobject(self.numpy_image, height=height)
         elif len(np.shape(self.numpy_image)) == 3:
             # Assumed RGB
-            self.image_mobject = ImageMobject(self.numpy_image)
+            self.image_mobject = ImageMobject(self.numpy_image).scale_to_fit_height(height)
         self.add(self.image_mobject)
 
     @override_animation(Create)
     def _create_override(self, **kwargs):
+        debug_mode = False
+        if debug_mode:
+            return FadeIn(SurroundingRectangle(self.image_mobject))
         return FadeIn(self.image_mobject)
 
     def make_forward_pass_animation(self):
-        return Create(self.image_mobject)
-
-    def move_to(self, location):
-        """Override of move to"""
-        self.image_mobject.move_to(location)
-
+        return FadeIn(self.image_mobject)
+    
+    # def move_to(self, location):
+    #     """Override of move to"""
+    #     self.image_mobject.move_to(location)
+    
     def get_right(self):
         """Override get right"""
         return self.image_mobject.get_right()
diff --git a/manim_ml/neural_network/layers/image_to_feed_forward.py b/manim_ml/neural_network/layers/image_to_feed_forward.py
index 86568f9..3cadaf7 100644
--- a/manim_ml/neural_network/layers/image_to_feed_forward.py
+++ b/manim_ml/neural_network/layers/image_to_feed_forward.py
@@ -10,7 +10,7 @@ class ImageToFeedForward(ConnectiveLayer):
 
     def __init__(self, input_layer, output_layer, animation_dot_color=RED,
                 dot_radius=0.05, **kwargs):
-        super().__init__(input_layer, output_layer, input_class=ImageLayer, output_class=FeedForwardLayer
+        super().__init__(input_layer, output_layer, input_class=ImageLayer, output_class=FeedForwardLayer,
                         **kwargs)
         self.animation_dot_color = animation_dot_color
         self.dot_radius = dot_radius
diff --git a/manim_ml/neural_network/layers/paired_query_to_feed_forward.py b/manim_ml/neural_network/layers/paired_query_to_feed_forward.py
index c1f6d97..deb2a10 100644
--- a/manim_ml/neural_network/layers/paired_query_to_feed_forward.py
+++ b/manim_ml/neural_network/layers/paired_query_to_feed_forward.py
@@ -9,7 +9,7 @@ class PairedQueryToFeedForward(ConnectiveLayer):
     output_class = FeedForwardLayer
 
     def __init__(self, input_layer, output_layer, animation_dot_color=RED, dot_radius=0.02, **kwargs):
-        super().__init__(input_layer, output_layer, input_class=PairedQueryLayer, output_class=FeedForwardLayer
+        super().__init__(input_layer, output_layer, input_class=PairedQueryLayer, output_class=FeedForwardLayer,
                         **kwargs)
         self.animation_dot_color = animation_dot_color
         self.dot_radius = dot_radius
diff --git a/manim_ml/neural_network/layers/parent_layers.py b/manim_ml/neural_network/layers/parent_layers.py
index c932e9c..2a4ab5d 100644
--- a/manim_ml/neural_network/layers/parent_layers.py
+++ b/manim_ml/neural_network/layers/parent_layers.py
@@ -23,7 +23,7 @@ class NeuralNetworkLayer(ABC, Group):
 class VGroupNeuralNetworkLayer(NeuralNetworkLayer):
 
     def __init__(self, **kwargs):
-        super(NeuralNetworkLayer, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
     @abstractmethod
     def make_forward_pass_animation(self):
diff --git a/manim_ml/neural_network/layers/util.py b/manim_ml/neural_network/layers/util.py
index 65654ba..a78cd3f 100644
--- a/manim_ml/neural_network/layers/util.py
+++ b/manim_ml/neural_network/layers/util.py
@@ -15,6 +15,6 @@ def get_connective_layer(input_layer, output_layer):
 
     if connective_layer is None:
         raise Exception(f"Unrecognized class pair {input_layer.__class__.__name__}" + \
-                        " and {output_layer.__class__.__name__}")
-                        
+                        f" and {output_layer.__class__.__name__}")
+
     return connective_layer
diff --git a/manim_ml/neural_network/layers/vector.py b/manim_ml/neural_network/layers/vector.py
index 585e946..5ec9875 100644
--- a/manim_ml/neural_network/layers/vector.py
+++ b/manim_ml/neural_network/layers/vector.py
@@ -8,13 +8,12 @@ class VectorLayer(VGroupNeuralNetworkLayer):
 
     def __init__(self, num_values, value_func=lambda: random.uniform(0, 1),
                 **kwargs):
-        print("vector layer")
         super().__init__(**kwargs)
-        print("after init")
         self.num_values = num_values
         self.value_func = value_func
         # Make the vector
         self.vector_label = self.make_vector()
+        self.add(self.vector_label)
 
     def make_vector(self):
         """Makes the vector"""
@@ -24,7 +23,8 @@ class VectorLayer(VGroupNeuralNetworkLayer):
             values = values[None, :].T
             vector = Matrix(values)
 
-        vector_label = Text(f"[{self.value_func()}]")
+        vector_label = Text(f"[{self.value_func():.2}]")
+        vector_label.scale(0.5)
 
         return vector_label
 
@@ -34,4 +34,4 @@ class VectorLayer(VGroupNeuralNetworkLayer):
     @override_animation(Create)
     def _create_override(self):
         """Create animation"""
-        return Create(self.vector_label)
\ No newline at end of file
+        return Write(self.vector_label)
\ No newline at end of file
diff --git a/manim_ml/neural_network/neural_network.py b/manim_ml/neural_network/neural_network.py
index 3017c28..873a2f3 100644
--- a/manim_ml/neural_network/neural_network.py
+++ b/manim_ml/neural_network/neural_network.py
@@ -10,7 +10,6 @@ Example:
     NeuralNetwork(layer_node_count)
 """
 from socket import create_connection
-from urllib.parse import non_hierarchical
 from manim import *
 import warnings
 import textwrap
@@ -19,12 +18,177 @@ from manim_ml.neural_network.layers import connective_layers_list
 from manim_ml.neural_network.layers.feed_forward import FeedForwardLayer
 from manim_ml.neural_network.layers.util import get_connective_layer
 from manim_ml.list_group import ListGroup
+
+class LazyAnimation(Animation):
+    """
+        Lazily creates animation when the animation is called.
+
+        This is helpful when creating the animation depends upon the internal
+        state of some set of objects. 
+    """
+
+    def __init__(self, animation_func):
+        self.animation_func = animation_func
+        super().__init__(None)
+
+    def begin(self):
+        """Begins animation"""
+        self.mobject, animation = self.animation_func()
+        animation = Create(self.mobject)
+        animation.begin()
+
+class RemoveLayer(Succession):
+    """
+        Animation for removing a layer from a neural network.
+
+        Note: I needed to do something strange for creating the new connective layer.
+        The issue with creating it intially is that the positions of the sides of the 
+        connective layer depend upon the location of the moved layers **after** the
+        move animations are performed. However, all of these animations are performed
+        after the animations have been created. This means that the animation depends upon
+        the state of the neural network layers after previous animations have been run. 
+        To fix this issue I needed to use an UpdateFromFunc.
+    """
+
+    def __init__(self, layer, neural_network, layer_spacing=0.2):
+        self.layer = layer
+        self.neural_network = neural_network
+        self.layer_spacing = layer_spacing
+        # Get the before and after layers
+        layers_tuple = self.get_connective_layers()
+        self.before_layer = layers_tuple[0]
+        self.after_layer = layers_tuple[1]
+        self.before_connective = layers_tuple[2]
+        self.after_connective = layers_tuple[3]
+        # Make the animations
+        remove_animations = self.make_remove_animation()
+        move_animations = self.make_move_animation()
+        new_connective_animation = self.make_new_connective_animation()
+        # Add all of the animations to the group
+        animations_list = [
+            remove_animations,
+            move_animations,
+            new_connective_animation
+        ]
+
+        super().__init__(*animations_list, lag_ratio=1.0)
+
+    def get_connective_layers(self):
+        """Gets the connective layers before and after self.layer"""
+        # Get layer index
+        layer_index = self.neural_network.all_layers.index_of(self.layer)
+        if layer_index == -1:
+            raise Exception("Layer object not found")
+        # Get the layers before and after
+        before_layer = None
+        after_layer = None
+        before_connective = None
+        after_connective = None
+        if layer_index - 2 >= 0:
+            before_layer = self.neural_network.all_layers[layer_index - 2]
+            before_connective = self.neural_network.all_layers[layer_index - 1]
+        if layer_index + 2 < len(self.neural_network.all_layers): 
+            after_layer = self.neural_network.all_layers[layer_index + 2]
+            after_connective = self.neural_network.all_layers[layer_index + 1]
+        
+        return before_layer, after_layer, before_connective, after_connective
+
+    def make_remove_animation(self):
+        """Removes layer and the surrounding connective layers"""
+        remove_layer_animation = self.make_remove_layer_animation()
+        remove_connective_animation = self.make_remove_connective_layers_animation()
+        # Remove animations
+        remove_animations = AnimationGroup(
+            remove_layer_animation, 
+            remove_connective_animation
+        )
+
+        return remove_animations
+
+    def make_remove_layer_animation(self):
+        """Removes the layer"""
+        # Remove the layer
+        self.neural_network.all_layers.remove(self.layer)
+        # Fade out the removed layer
+        fade_out_removed = FadeOut(self.layer)
+        return fade_out_removed
+
+    def make_remove_connective_layers_animation(self):
+        """Removes the connective layers before and after layer if they exist"""
+        # Fade out the removed connective layers
+        fade_out_before_connective = AnimationGroup()
+        if not self.before_connective is None:
+            self.neural_network.all_layers.remove(self.before_connective)
+            fade_out_before_connective = FadeOut(self.before_connective)
+        fade_out_after_connective = AnimationGroup()
+        if not self.after_connective is None:
+            self.neural_network.all_layers.remove(self.after_connective)
+            fade_out_after_connective = FadeOut(self.after_connective)
+        # Group items
+        remove_connective_group = AnimationGroup(
+            fade_out_after_connective, 
+            fade_out_before_connective
+        )
+
+        return remove_connective_group
+
+    def make_move_animation(self):
+        """Collapses layers"""
+        # Animate the movements
+        move_before_layers = AnimationGroup()
+        shift_right_amount = None
+        if not self.before_layer is None:
+            # Compute shift amount
+            layer_dist = np.abs(self.layer.get_center() - self.before_layer.get_right())[0]
+            shift_right_amount = np.array([layer_dist - self.layer_spacing/2, 0, 0])
+            # Shift all layers before forward
+            before_layer_index = self.neural_network.all_layers.index_of(self.before_layer)
+            layers_before = Group(*self.neural_network.all_layers[:before_layer_index + 1])
+            move_before_layers = layers_before.animate.shift(shift_right_amount)
+        move_after_layers = AnimationGroup()
+        shift_left_amount = None
+        if not self.after_layer is None:
+            layer_dist = np.abs(self.after_layer.get_left() - self.layer.get_center())[0]
+            shift_left_amount = np.array([-layer_dist + self.layer_spacing / 2, 0, 0])
+            # Shift all layers after backward
+            after_layer_index = self.neural_network.all_layers.index_of(self.after_layer)
+            layers_after = Group(*self.neural_network.all_layers[after_layer_index:])
+            move_after_layers = layers_after.animate.shift(shift_left_amount)
+        # Group the move animations
+        move_group = AnimationGroup(
+            move_before_layers, 
+            move_after_layers
+        )
+
+        return move_group
+
+    def make_new_connective_animation(self):
+        """Makes new connective layer"""
+        self.anim_count = 0
+        def create_new_connective(neural_network):
+            """
+                Creates new connective layer
+            
+                This is a closure that creates a new connective layer and animates it.
+            """
+            self.anim_count += 1
+            if self.anim_count == 1:
+                if not self.before_layer is None and not self.after_layer is None:
+                    print(neural_network)
+                    new_connective = get_connective_layer(self.before_layer, self.after_layer)
+                    before_layer_index = neural_network.all_layers.index_of(self.before_layer) + 1
+                    neural_network.all_layers.insert(before_layer_index, new_connective)
+                    print(neural_network)
+
+        update_func_anim = UpdateFromFunc(self.neural_network, create_new_connective)
+
+        return update_func_anim
     
 class NeuralNetwork(Group):
 
-    def __init__(self, input_layers, edge_color=WHITE, layer_spacing=0.8,
+    def __init__(self, input_layers, edge_color=WHITE, layer_spacing=0.2,
                     animation_dot_color=RED, edge_width=2.5, dot_radius=0.03,
-                    title="Overhead Title"):
+                    title=" "):
         super(Group, self).__init__()
         self.input_layers = ListGroup(*input_layers)
         self.edge_width = edge_width
@@ -32,7 +196,7 @@ class NeuralNetwork(Group):
         self.layer_spacing = layer_spacing
         self.animation_dot_color = animation_dot_color
         self.dot_radius = dot_radius
-        self.title = title
+        self.title_text = title
         self.created = False
         # TODO take layer_node_count [0, (1, 2), 0] 
         # and make it have explicit distinct subspaces
@@ -42,9 +206,9 @@ class NeuralNetwork(Group):
         self.layer_titles = self._make_layer_titles()
         self.add(self.layer_titles)
         # Make overhead title
-        self.overhead_title = Text(self.title, font_size=DEFAULT_FONT_SIZE/2)
-        self.overhead_title.next_to(self, UP, 0.2)
-        self.add(self.overhead_title)
+        self.title = Text(self.title_text, font_size=DEFAULT_FONT_SIZE/2)
+        self.title.next_to(self, UP, 1.0)
+        self.add(self.title)
         # Place layers at correct z index
         self.connective_layers.set_z_index(2)
         self.input_layers.set_z_index(3)
@@ -62,7 +226,7 @@ class NeuralNetwork(Group):
             previous_layer = self.input_layers[layer_index - 1]
             current_layer = self.input_layers[layer_index]
             current_layer.move_to(previous_layer)
-            shift_vector = np.array([(previous_layer.get_width()/2 + current_layer.get_width()/2) + 0.2, 0, 0])
+            shift_vector = np.array([(previous_layer.get_width()/2 + current_layer.get_width()/2) + self.layer_spacing, 0, 0])
             current_layer.shift(shift_vector)
 
     def _make_layer_titles(self):
@@ -137,132 +301,8 @@ class NeuralNetwork(Group):
 
     def remove_layer(self, layer):
         """Removes layer object if it exists"""
-        # Get layer index
-        layer_index = self.all_layers.index_of(layer)
-        if layer_index == -1:
-            raise Exception("Layer object not found")
-        # Get the layers before and after
-        before_layer = None
-        after_layer = None
-        if layer_index - 2 >= 0:
-            before_layer = self.all_layers[layer_index - 2]
-        if layer_index + 2 < len(self.all_layers): 
-            after_layer = self.all_layers[layer_index + 2]
-        # Remove the layer
-        self.all_layers.remove(layer)
-        # Remove surrounding connective layers from self.all_layers
-        before_connective = None
-        after_connective = None
-        if layer_index - 1 >= 0:
-            # There is a layer before
-            before_connective = self.all_layers.remove_at_index(layer_index - 1)
-        if layer_index + 1 < len(self.all_layers):
-            # There is a layer after
-            after_connective = self.all_layers.remove_at_index(layer_index + 1)
-        # Make animations
-        # Fade out the removed layer
-        fade_out_removed = FadeOut(layer)
-        # Fade out the removed connective layers
-        fade_out_before_connective = Animation()
-        if not before_connective is None:
-            fade_out_before_connective = FadeOut(before_connective)
-        fade_out_after_connective = Animation()
-        if not after_connective is None:
-            fade_out_after_connective = FadeOut(after_connective)
-        # Create new connective layer
-        new_connective = None
-        if not before_layer is None and not after_layer is None:
-            new_connective = get_connective_layer(before_layer, after_layer)
-            before_layer_index = self.all_layers.index_of(before_layer)
-            self.all_layers.insert(before_layer_index, new_connective)
-        # Place the new connective
-        new_connective.move_to(layer)
-        # Animate the creation of the new connective layer
-        create_new_connective = Animation()
-        if not new_connective is None:
-            create_new_connective = Create(new_connective)
-        # Collapse the neural network to fill the empty space
-        removed_width = layer.width + before_connective.width + after_connective.width - new_connective.width
-        shift_right_amount = np.array([removed_width / 2, 0, 0])
-        shift_left_amount = np.array([-removed_width / 2, 0, 0])
-        move_before_layer = Animation()
-        if not before_layer is None:
-            move_before_layer = before_layer.animate.shift(shift_right_amount)
-        move_after_layer = Animation()
-        if not after_layer is None:
-            move_after_layer = after_layer.animate.shift(shift_left_amount)
-        # Make the final AnimationGroup
-        fade_out_group = AnimationGroup(
-            fade_out_removed, 
-            fade_out_before_connective, 
-            fade_out_after_connective
-        )
-        move_group = AnimationGroup(
-            move_before_layer, 
-            move_after_layer
-        )
-        animation_group = AnimationGroup(
-            fade_out_group,
-            move_group,
-            create_new_connective,
-            lag_ratio=1.0
-        )
-
-        return animation_group
-
-        """
-        remove_layer = list(self.all_layers)[remove_index]
-        if remove_index > 0:
-            connective_before = list(self.all_layers)[remove_index - 1]
-        else:
-            connective_before = None
-        if remove_index < len(list(self.all_layers)) - 1:
-            connective_after = list(self.all_layers)[remove_index + 1]
-        else:
-            connective_after = None
-        # Collapse the surrounding layer
-        layers_before = list(self.all_layers)[:remove_index]
-        layers_after = list(self.all_layers)[remove_index+1:]
-        before_group = Group(*layers_before)
-        after_group = Group(*layers_after)
-        before_shift_amount = np.array([remove_layer.width/2, 0, 0])
-        after_shift_amount = np.array([-remove_layer.width/2, 0, 0])
-        # Remove the layers from the neural network representation
-        self.all_layers.remove(remove_layer)
-        if not connective_before is None:
-            self.all_layers.remove(connective_before)
-        if not connective_after is None:
-            self.all_layers.remove(connective_after)
-        # Connect the layers before and layers after
-        pre_index = remove_index - 1
-        pre_layer = None
-        if pre_index >= 0:
-            pre_layer = list(self.all_layers)[pre_index]
-        post_index = remove_index
-        post_layer = None
-        if post_index < len(list(self.all_layers)):
-            post_layer = list(self.all_layers)[post_index]
-        if not pre_layer is None and not post_layer is None:
-            connective_layer = get_connective_layer(pre_layer, post_layer)
-            self.all_layers = Group(
-                *self.all_layers[:remove_index], 
-                connective_layer, 
-                *self.all_layers[remove_index:]
-            )
-        # Make animations
-        fade_out_animation = FadeOut(remove_layer)
-        shift_animations = AnimationGroup(
-            before_group.animate.shift(before_shift_amount),
-            after_group.animate.shift(after_shift_amount)
-        )
-        animation_group = AnimationGroup(
-            fade_out_animation,
-            shift_animations,
-            lag_ratio=1.0
-        )
-
-        return animation_group
-        """
+        neural_network = self
+        return RemoveLayer(layer, neural_network, layer_spacing=self.layer_spacing)
 
     def replace_layer(self, old_layer, new_layer):
         """Replaces given layer object"""
@@ -304,7 +344,7 @@ class NeuralNetwork(Group):
 
         animations = []
         # Create the overhead title
-        animations.append(Write(self.overhead_title))
+        animations.append(Create(self.title))
         # Create each layer one by one
         for layer in self.all_layers:
             layer_animation = Create(layer)
@@ -318,11 +358,15 @@ class NeuralNetwork(Group):
             animations.append(animation_group)
 
         animation_group = AnimationGroup(*animations, lag_ratio=1.0)
-        print(animation_group)
         
         return animation_group
 
-    def __repr__(self, metadata=["z_index", "title"]):
+    def set_z_index(self, z_index_value: float, family=False):
+        """Overriden set_z_index"""
+        # Setting family=False stops sub-neural networks from inheriting parent z_index
+        return super().set_z_index(z_index_value, family=False)
+
+    def __repr__(self, metadata=["z_index", "title_text"]):
         """Print string representation of layers"""
         inner_string = ""
         for layer in self.all_layers: