fn main() raises:
var x = Tensor[DType.float32] (1,2,5)
var y = Tensor[DType.float32] (1,2,5)
var in_types = List[Type] (TensorType(DType.float32, "a", "b", "c"), TensorType(DType.float32, "a", "b", "c"))
var graph10 = Graph(in_types=in_types)
var inputs = List[Symbol] (graph10[0], graph10[1])
var c = ops.concat(inputs, -1)
graph10.output(c)
graph10.verify()
var session = engine.InferenceSession()
var concat = session.load(graph10)
var results = concat.execute("input0", x, "input1", y)
var xd = results.get[DType.float32] ("output0")
print(x.shape())
print(y.shape())
print(xd.shape())
fn main() raises:
var x = Tensor[DType.float32] (1,2,5)
var y = Tensor[DType.float32] (1,2,5)
var in_types = List[Type] (TensorType(DType.float32, "a", "b", "c"), TensorType(DType.float32, "a", "b", "c"))
var graph10 = Graph(in_types=in_types)
var inputs = List[Symbol] (graph10[0], graph10[1])
var c = ops.concat(inputs, -1)
graph10.output(c)
graph10.verify()
var session = engine.InferenceSession()
var concat = session.load(graph10)
var results = concat.execute("input0", x, "input1", y)
var xd = results.get[DType.float32] ("output0")
print(x.shape())
print(y.shape())
print(xd.shape())