You could convert the tensors to numpy arrays and apply the colormap on them:
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import cm


x = np.random.randn(100, 100).clip(0, 1)

magma = cm.get_cmap('magma')
x_transformed = magma(x)
plt.imshow(x_transformed)

plt.imshow(x)
plt.imshow(x, cmap='magma')

Note that the colormap expects normalized inputs, so I clipped the data to [0, 1].
Once you have these transformed arrays, you could convert them back to tensors via torch.from_numpy.
PS: What is your use case that you want to artificially inflate your channels?