}

    def "repositories have the same id when m2 compatible and other configuration is the same"() {
        def repo = newRepo()
        def same = newRepo()
        def different = newRepo()

        given:
        repo.url = new URI("http://localhost")
        repo.patternLayout { it.m2compatible = true }
        same.url = new URI("http://localhost")
        same.patternLayout { it.m2compatible = true }

  // CHECK-SAME: _XlaSharding = ""
  // CHECK-SAME: partition_dims = []
  // CHECK-DAG:  "tf.AssignVariableOp"([[ARG0]], [[OUTPUT]]#0)
  // CHECK-DAG:  "tf.AssignVariableOp"([[ARG1]], [[OUTPUT]]#1)
  "tf.AssignVariableOp"(%0, %2) : (tensor<!tf_type.resource<tensor<i32>>>, tensor<i32>) -> ()
  func.return
}

// CHECK-LABEL: func @read_write_packed_resource

// RUN: tf-opt -split-input-file -verify-diagnostics -xla-legalize-tf-collective -xla-legalize-tf %s | FileCheck %s


// CHECK: module attributes
// CHECK-SAME{LITERAL}: tf2xla.collective_info.group_key = 0
// CHECK-SAME{LITERAL}: tf2xla.collective_info.group_size = 2
// CHECK-LABEL: func @all_reduce_cross_replica
func.func @all_reduce_cross_replica(%input: tensor<f32>) -> tensor<f32> {

// it is not nil if the result does not escape.
//
// slicebytetostring should be an internal detail,
// but widely used packages access it using linkname.
// Notable members of the hall of shame include:
//   - github.com/cloudwego/frugal
//
// Do not remove or change the type signature.
// See go.dev/issue/67401.
//
//go:linkname slicebytetostring
func slicebytetostring(buf *tmpBuf, ptr *byte, n int) string {

  %0 = "tf_device.cluster"() ({
    // CHECK: "tf.Const"() <{value = dense<1> : tensor<i32>}>
    // CHECK-NOT: _xla_outside_compilation
    // CHECK: "tf.Const"
    // CHECK-SAME: _xla_outside_compilation
    // CHECK-SAME: tf_type.string
    // CHECK: "tf.Identity"
    // CHECK-NOT: _xla_outside_compilation
    %1 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>

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"

// 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"]

  func.return
}

// -----

// Tests last resource assign is returned even when the original function
// returns the same value prior.

// CHECK-LABEL: func @main
// CHECK-SAME: %{{[a-z0-9]+}}: tensor<f32> {tf.aliasing_output = 1 : i64}
// CHECK-SAME: %arg1: tensor<i1>
// CHECK-SAME: -> (tensor<f32>, tensor<f32>)
func.func @main(%arg0: tensor<!tf_type.resource<tensor<f32>>>, %arg1: tensor<i1>) -> tensor<f32> {

  func.return %2 : tensor<1x128x128x8xf32>

  // CHECK-LABEL: testDilatedConvWithNonZeroBasePadding
  // CHECK-SAME: ([[INPUT:%.*]]: tensor<1x128x128x3xf32>, [[FILTER:%.*]]: tensor<5x5x3x8xf32>)
  // CHECK-NEXT: [[RESULT:%.*]] = "tf.Conv2D"([[INPUT]], [[FILTER]]) <{dilations = [1, 2, 2, 1], padding = "SAME", strides = [1, 1, 1, 1]}> : (tensor<1x128x128x3xf32>, tensor<5x5x3x8xf32>) -> tensor<1x128x128x8xf32>

    // CHECK-SAME: (tensor<2xi32>) -> tensor<1xi32>
    // CHECK: %[[DIM0:.*]] = mhlo.reshape %[[SLICE0]] : (tensor<1xi32>) -> tensor<i32>

    // CHECK: %[[SLICE1:.*]] = "mhlo.slice"(%[[ARG2]])
    // CHECK-DAG-SAME: start_indices = dense<1> : tensor<1xi64>
    // CHECK-DAG-SAME: limit_indices = dense<2> : tensor<1xi64>
    // CHECK-DAG-SAME: strides = dense<1> : tensor<1xi64>