<｜begin▁of▁sentence｜>### Instruction:
Imagine we're working on an ecological connectivity analysis using a simplified model of a nature reserve represented by a linear arrangement of habitats, coded as nodes 0 through 5. These habitats are connected by natural corridors, which are akin to the edges in our model. For certain research or conservation actions, we might want to focus on a subset of these habitats while considering the impact corridors, marked by the attribute 'cross_me'. 

Assuming we've already surveyed the area and identified that the corridor between habitats 3 and 4 is currently disrupted, possibly due to human activity, and this disruption has been denoted by setting the 'cross_me' attribute to False in our data model. Now, to generate a refined model for our conservation planning that focuses only on the unaffected habitats and corridors, we need to create a subgraph. 

This subgraph should represent only those habitats and corridors that remain functional. Specifically, we want the new model to exclude habitat 5 and any corridors where the 'cross_me' attribute is False (like the one between habitats 3 and 4). 

We'd use the subgraph_view function from the NetworkX library to create this ecological subgraph. Could you illustrate the process of attaining this ecological subgraph using NetworkX, based on the predefined criteria, without actually constructing it in this communication? Remember, our conservation efforts depend on accurately representing our data to make the best management decisions for the nature reserve.
### Response:
import networkx as nx

# Create a graph
G = nx.path_graph(6)

# Filter function that excludes node 5
filter_node = lambda n: n != 5

# Filter function that excludes an edge if it has the attribute 'cross_me' set to False
filter_edge = lambda n1, n2: G[n1][n2].get('cross_me', True)

# Add the 'cross_me' attribute to the edge (3, 4)
G[3][4]['cross_me'] = False

# Create a subgraph view using the filters
view = nx.subgraph_view(G, filter_node=filter_node, filter_edge=filter_edge)

# Print nodes and edges of the view
print('Nodes:', list(view.nodes))
print('Edges:', list(view.edges))
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