<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a naturalist working on a fascinating project where your goal is to create an engaging and educational exhibit on the diverse ecosystems and their inhabitants. You've painstakingly collected a dataset named 'items_features' that encapsulates the Euclidean feature distances between various species, illustrating how similar or different they are based on certain ecological and biological traits. To make this information accessible and understandable to visitors of all ages, you've considered creating a simplified, yet informative, visual representation of these relationships.

Your task is now to employ a technique known as Classical Multidimensional Scaling (MDS) using the graspologic.embed API, specifically the ClassicalMDS tool, to translate this high-dimensional data into a low-dimensional space. This process will help you generate a map that visualizes the proximity of species in a more digestible format.

Could you proceed by transforming the 'items_features' dataset using ClassicalMDS from graspologic.embed, and then share the new coordinates? This will be a crucial step in developing an exhibit that not only enlightens but also captivates the museum's visitors, offering them a unique glimpse into the natural world's intricate web of life.
### Response:
import numpy as np
from graspologic.embed import ClassicalMDS
from sklearn.metrics import pairwise_distances

# Assume we have a dataset of items with their features
items_features = np.array([
    [1, 2],
    [3, 4],
    [5, 6],
])

# Compute dissimilarities using euclidean distance
# Normally ClassicalMDS expects a dissimilarity matrix
items_dissimilarities = pairwise_distances(items_features, metric='euclidean')

# Initialize the ClassicalMDS model
# Here we specify we want 2 components as the output of the embedding
mds = ClassicalMDS(n_components=2)

# Fit the model and transform items_features into a 2-dimensional space
items_embedding = mds.fit_transform(items_dissimilarities)

print(items_embedding)
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