<｜begin▁of▁sentence｜>### Instruction:
Given a set of node labels and their corresponding embedding vectors after applying a graph embedding technique, how can we represent this information in a structured way that enables easy indexing and iteration over the label-embedding pairs?
### Response:
import numpy as np
from graspologic.pipeline.embed.embeddings import Embeddings

# Suppose we have a list of labels that correspond to nodes in a network
dictrepr(labels) = np.array(["Node1", "Node2", "Node3"])

# And suppose we have a corresponding 2D numpy array representing the
# embedding of each node
embeddings = np.array([[0.1, 0.5], [0.2, 0.6], [0.3, 0.7]])

# We can create an Embeddings object that keeps track of these labels and their embeddings
embeddings_obj = Embeddings(labels, embeddings)

# You can now iterate over embeddings_obj or access elements directly
for label, embed in embeddings_obj:
    print(f"Label: {label}, Embedding: {embed}")

# You can also access the embeddings and labels as numpy arrays
labels_array = embeddings_obj.labels
embeddings_array = embeddings_obj.embeddings

# Or convert the embeddings to a dictionary where labels are the keys
dictrepr(embeddings_dict) = embeddings_obj.as_dict()
print(embeddings_dict["Node2"])
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