<｜begin▁of▁sentence｜>### Instruction:
Given that we have a set of labels and their associated embeddings in a high-dimensional space, how can we create an indexed representation using the Embeddings class in graspologic and perform operations such as accessing a specific embedding by label or iterating through all embeddings?
### Response:
import numpy as np
from graspologic.pipeline.embed import Embeddings

raw_embeddings = np.array([[1, 2, 3, 4, 5], [6, 4, 2, 0, 8]])
labels = np.array(['node1', 'node2'])
embeddings_obj = Embeddings(labels, raw_embeddings)

# Access the first embedding
first_embedding = embeddings_obj[0]

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

# Iterate over embeddings
embeddings_list = list(iter(embeddings_obj))

# Convert the embeddings to a dictionary
embeddings_lookup = embeddings_obj.as_dict()

# Retrieve embedding by node label
specific_embedding = embeddings_lookup['node2']
print('First embedding:', first_embedding)
print('Number of embeddings:', embeddings_length)
print('List of embeddings:', embeddings_list)
print('Specific node embedding:', specific_embedding)
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