How to use the colorcet.fire function in colorcet

import bokeh.plotting as bpl
import bokeh.transform as btr
import holoviews as hv
import holoviews.operation.datashader as hd

import sklearn.decomposition
import sklearn.cluster
import sklearn.neighbors

from umap.nndescent import initialise_search, initialized_nnd_search
from umap.utils import deheap_sort, submatrix

from bokeh.plotting import output_notebook, output_file, show
from warnings import warn

fire_cmap = matplotlib.colors.LinearSegmentedColormap.from_list('fire', colorcet.fire)
darkblue_cmap = matplotlib.colors.LinearSegmentedColormap.from_list('darkblue', colorcet.kbc)
darkgreen_cmap = matplotlib.colors.LinearSegmentedColormap.from_list('darkgreen', colorcet.kgy)
darkred_cmap = matplotlib.colors.LinearSegmentedColormap.from_list('darkred',
                                                                   colors=colorcet.linear_kry_5_95_c72[:192],
                                                                   N=256)
darkpurple_cmap = matplotlib.colors.LinearSegmentedColormap.from_list('darkpurple',
                                                                      colorcet.linear_bmw_5_95_c89)

plt.register_cmap('fire', fire_cmap)
plt.register_cmap('darkblue', darkblue_cmap)
plt.register_cmap('darkgreen', darkgreen_cmap)
plt.register_cmap('darkred', darkred_cmap)
plt.register_cmap('darkpurple', darkpurple_cmap)


def _to_hex(arr):

import dask.dataframe as dd
import holoviews as hv
import parambokeh
import param

from colorcet import cm_n, fire

from bokeh.models import Slider, Button
from bokeh.layouts import layout
from bokeh.io import curdoc

from holoviews.operation.datashader import aggregate, shade
from holoviews.element.tiles import EsriImagery


shade.cmap = fire

hv.extension('bokeh')
renderer = hv.renderer('bokeh').instance(mode='server')

# Load data
ddf = dd.read_parquet(os.path.join(os.path.dirname(__file__),'..','..','..','data','nyc_taxi_wide.parq', engine='fastparquet')).persist()
tiles = EsriImagery()

stream = hv.streams.Stream.define('HourSelect', hour=0)()
points = hv.Points(ddf, kdims=['dropoff_x', 'dropoff_y'])
dmap = hv.util.Dynamic(points, operation=lambda obj, hour: obj.select(dropoff_hour=hour).relabel('Hour of Day: %d' % hour),
                       streams=[stream])

# Apply aggregation
aggregated = aggregate(dmap, link_inputs=True, streams=[hv.streams.RangeXY], width=1200, height=600)