---
title: "argument(name:dataType:shape:intent:)"
framework: accelerate
role: symbol
role_heading: Instance Method
path: "accelerate/bnnsgraph/builder/argument(name:datatype:shape:intent:)"
---

# argument(name:dataType:shape:intent:)

Registers and returns an input or in-out tensor argument to the graph.

## Declaration

```swift
func argument<T>(name: String? = nil, dataType: T.Type, shape: [Int], intent: BNNSGraph.Builder.Intent = .input) -> BNNSGraph.Builder.Tensor<T> where T : BNNSScalar
```

## Parameters

- `name`: The name of the argument.
- `dataType`: The argument’s data type.
- `shape`: An array of integers or closed ranges that define the sizes of each dimension of the argument. Pass a negative value to specify a flexible dimension.
- `intent`: A constant that specifies whether the argument is input, output, or in-out.

## Discussion

Discussion The following code shows how to specify inputs with flexible shapes. The code calls setBatchSize before executing the context to specify that the two inputs and the output tensors are 8-element vectors:        let context = try? BNNSGraph.makeContext ({            builder in

let x = builder.argument(name: "x",                                     dataType: Float.self,                                     shape: [-1])            let y = builder.argument(name: "y",                                     dataType: Float.self,                                     shape: [-1])

let z = x + y

return [z]        })

var args = context.argumentNames().map {            name in

return context.tensor(argument: name,                                  fillKnownDynamicShapes: false)!        }

defer {            args.forEach {                $0.deallocate()            }        }

let n = 8

context.setBatchSize(n)

args[context.argumentPosition(argument: "x")]            .allocate(initializingFrom: [1, 2, 3, 4, 5, 6, 7, 8] as [Float])

args[context.argumentPosition(argument: "y")]            .allocate(initializingFrom: [10, 20, 30, 40, 50, 60, 70, 80] as [Float])

// Output argument        args[0].allocate(as: Float.self, count: n)

try context.executeFunction(arguments: &args)        ``` On return, the output tensor contains the values `[11, 22, 33, 44, 55, 66, 77, 88]`.