vinx13

import tvm
import tvm.relay as relay
import tvm.relay.testing
import numpy as np

x = relay.var("x", shape=(1, 16))
y = relay.var("y", shape=(1, 16))
z = relay.var("z", shape=(1, 16))
cond = relay.var("cond", shape=(), dtype='uint1')
net = relay.If(cond, x, y)

vinx13

import numpy as np
import logging
import argparse
import os
import mxnet as mx
from mxnet import gluon
from mxnet.gluon.model_zoo import vision
import tvm
import tvm.relay as relay
import tvm.relay.expr as _expr

vinx13

import numpy as np
import argparse
import tvm
import tvm.relay as relay
import tvm.relay.testing
from tvm.relay.testing import layers
import mxnet as mx
from mxnet import gluon
from mxnet.gluon.model_zoo import vision

vinx13

import tvm
import tvm.relay as relay

x = relay.var("x", shape=(16,), dtype='float32')
y = relay.var("y", shape=(16,), dtype='float32')
c = relay.Let(x, y, x)

f =  relay.Function([y], relay.expr.Tuple([c, c]))
mod = relay.Module.from_expr(f)
mod = relay.transform.PartialEvaluate()(mod)

vinx13

import tvm
from tvm import tir
from tvm.script import ty

@tvm.script.tir
def foo(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
    A = tir.match_buffer(a, (128,), "float32")
    B = tir.match_buffer(b, (128,), "int32")
    C = tir.match_buffer(c, (128,), "float32")
    reducer = tir.comm_reducer(lambda x, y: (x + y), tir.float32(0))