<｜begin▁of▁sentence｜>### Instruction:
In the bustling world of public relations, where data, trends, and narratives intertwine, let's imagine a PR specialist in the spotlight, embarking on a cutting-edge journey to map the complex network of media influences and interactions. To navigate this intricate landscape, the specialist employs a state-of-the-art technique that transforms the vast web of connections into a comprehensible format. Picture each media outlet, blogger, and influencer as a node within a dynamic network, their relationships and interactions forming an elaborate graph. Through an innovative process, every node is translated into a numerical vector, encapsulating its position within the network and grounding it in a digital embedding space.

Now, with the aim of refining their strategic outreach and engagement, our specialist seeks an efficient method to associate these numerical vectors - the digital fingerprints of each entity within the network - with their corresponding labels, be it the names of influential bloggers or the categories defining media outlets. This is where the `Embeddings` class from graspologic comes into play, promising to serve as the linchpin in organizing this wealth of information. The specialist envisions leveraging graspologic to construct a structured database, wherein each label is seamlessly linked to its embedding vector, thereby ensuring immediate access to the precise digital location of any node on demand.

In this scenario, how can our PR maestro harness the `Embeddings` class from graspologic to curate and retrieve the embedding information for each label, ensuring that their strategic decisions are informed by the most accurate and accessible data?
### Response:
import numpy as np
from graspologic.pipeline.embed import Embeddings

# Suppose we have performed some embedding on graph data and obtained the following
raw_embeddings = np.array([[1, 2, 3, 4, 5], [6, 4, 2, 0, 8]])
labels = np.array(['monotonic', 'not_monotonic'])

# Create an instance of Embeddings
embeddings_obj = Embeddings(labels, raw_embeddings)

# Access the first embedding
first_embedding = embeddings_obj[0] # ('monotonic', array([1, 2, 3, 4, 5]))

# Get the length of the embeddings
embeddings_length = len(embeddings_obj) # 2

# Iterate over the embeddings
embeddings_list = list(iter(embeddings_obj)) # [('monotonic', array([1, 2, 3, 4, 5])), ('not_monotonic', array([6, 4, 2, 0, 8]))]

# Convert embeddings to a dictionary
embeddings_lookup = embeddings_obj.as_dict()
# Access embeddings by label
not_monotonic_embedding = embeddings_lookup['not_monotonic'] # array([6, 4, 2, 0, 8])
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