You can grab it in this way:
def some_fn(x):
	return x + x

some_input = torch.randn(2, 2)
my_traced_function = torch.jit.trace(some_fn, [some_input])

for input in my_traced_function.graph.inputs():
	traced_tensor_type = input.type()
	# Prints "Float"
	print(traced_tensor_type.scalarType())

# However, note that the interpreter will still run with differently typed
# tensors
my_traced_function(torch.ones(2, 2, dtype=torch.long))