<｜begin▁of▁sentence｜>### Instruction:
Imagine you're crafting a captivating tutorial video for your audience, where you aim to take them on a virtual journey through a network of interconnected concepts, each represented by a unique node label. Alongside these labels, you possess a treasure trove of dimensional embeddings that paint a deeper picture of the relationships between these nodes.

Now, your task is to weave a story around how one might encapsulate this intricate web using the tools provided by the graspologic library. Specifically, your audience is eager to learn how to forge an 'Embeddings' object, which would become their magic lens to glance at the hidden layers of nodes, glide smoothly between the embeddings, and on a whim, pinpoint the exact embedding tied to a given node label.

Think of this 'Embeddings' object as a mystical map, one that supports the intrepid explorers of your digital realm to navigate, query and apprehend the essence of each node through its multidimensional embedding with a simple call to an API.

Conjure up your narrative around this scenario, ensuring that your viewers can virtually feel how to preserve the knowledge embedded in node labels and their corresponding embeddings using graspologic's functionalities, without delving into the spellwork – I mean, the specific coding details.
### Response:
import numpy as np
from graspologic.pipeline.embed.embeddings import Embeddings
raw_embeddings = np.array([[1,2,3,4,5], [6,4,2,0,8]])
labels = np.array(['monotonic', 'not_monotonic'])
embeddings_obj = Embeddings(labels, raw_embeddings)
# Access the first embedding
print(embeddings_obj[0])
# Get the length of Embeddings object
print(len(embeddings_obj))
# Iterate over Embeddings object
for label, embedding in embeddings_obj:
    print(f'Label: {label}, Embedding: {embedding}')
# Create dictionary from Embeddings object
embeddings_lookup = embeddings_obj.as_dict()
# Access the embedding by label
print(embeddings_lookup['not_monotonic'])
<|EOT|>
