Dependency version alignment allows different modules belonging to the same logical group (a _platform_) to have identical versions in a dependency graph.

== Handling inconsistent module versions

Gradle supports aligning versions of modules which belong to the same "platform".
It is often preferable, for example, that the API and implementation modules of a component are using the same version.

  %cst_0 = "tf.Const"() {value = dense<0.500000e+00> : tensor<2xf32>} : () -> tensor<2xf32>
  %0 = "tf.Conv2D"(%arg0, %cst) {data_format = "NHWC", dilations = [1, 1, 2, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x2x2xf32>
  %1 = "tf.AddV2"(%0, %cst_0) : (tensor<1x3x2x2xf32>, tensor<2xf32>) -> tensor<1x3x2x2xf32>

            }
        }
        createTar('test2.tar') {
            subdir1 {
                file ('file.txt').text = 'original text 1' // Same name in same dir
            }
            subdir3 {
                file('file3.txt').text = 'original text 3' // Same name in same different nested dir
                file ('file4.txt').text =  'original text 4'
            }
        }

  // CHECK-LABEL: func @head_single_outside_compiled_op
  func.func @head_single_outside_compiled_op(%arg0: tensor<i32>) {
    // CHECK:      "tf_device.launch"
    // CHECK-SAME: device = "/job:worker/replica:0/task:0/device:CPU:0"
    // CHECK-NEXT:   "tf.A"
    // CHECK-NOT:    _xla_outside_compilation
    // CHECK-NEXT:   tf_device.return
    //
    // CHECK:      "tf_device.cluster"

But it's still part of the same **FastAPI** application/web API (it's part of the same "Python Package").

You can create the *path operations* for that module using `APIRouter`.

### Import `APIRouter`

You import it and create an "instance" the same way you would with the class `FastAPI`:

```Python hl_lines="1  3" title="app/routers/users.py"

func.func @set_stateless(%arg: tensor<i32>, %cond: tensor<i1>) -> (tensor<i32>, tensor<i32>) {
  // CHECK: tf.While
  // CHECK-SAME: is_stateless = true
  %0 = "tf.While"(%arg) { cond = @no_side_effect_cond, body = @no_side_effect_body, is_stateless = false} : (tensor<i32>) -> (tensor<i32>)
  // CHECK: tf.If
  // CHECK-SAME: is_stateless = true

      CHECK-SAME        loc("tfr_gen_test.py":%[[add_line]]:12)
      CHECK-NEXT:     scf.yield %[[Add]] : !tfr.tensor
      CHECK-SAME        loc(unknown)
      CHECK-NEXT:   }
      CHECK-SAME        loc("tfr_gen_test.py":%[[for_line]]:2)
      CHECK-NEXT:   %{{.*}} = arith.constant true
      CHECK-SAME        loc(unknown)
      CHECK-NEXT:   tfr.return %[[for_stmt]] : !tfr.tensor
      CHECK-SAME        loc(unknown)

// CHECK: func.func @main(%[[ARG:.*]]: tensor<?x!tf_type.string>
// CHECK-SAME: tf_saved_model.index_path = ["serving_default_input_vocabs:0"]
// CHECK-SAME: -> (tensor<*xi64> {tf_saved_model.index_path = ["StatefulPartitionedCall:0"]})
// CHECK-SAME: tf.entry_function = {inputs = "serving_default_input_vocabs:0", outputs = "StatefulPartitionedCall:0"}
// CHECK-SAME: tf_saved_model.exported_names = ["main"]

			emon, epool, eimage := existingVolume.RBD.CephMonitors, existingVolume.RBD.RBDPool, existingVolume.RBD.RBDImage
			// two RBDs images are the same if they share the same Ceph monitor, are in the same RADOS Pool, and have the same image name
			// only one read-write mount is permitted for the same RBD image.
			// same RBD image mounted by multiple Pods conflicts unless all Pods mount the image read-only

  // CHECK: tfrt.while [[pred_0]]#1 @"while_body_add2/tfrt_body_10"
  // CHECK-SAME: parallel_iterations(10)
  // CHECK: [[pred_1:%.*]]:2 = tfrt.call @"while_cond_lt9/tfrt_predicate"
  // CHECK: tfrt.while [[pred_1]]#1 @"while_body_add2/tfrt_body_1"
  // CHECK-SAME: parallel_iterations(1)