// to be the same from one call to the next.
func All[Map ~map[K]V, K comparable, V any](m Map) iter.Seq2[K, V] {
	return func(yield func(K, V) bool) {
		for k, v := range m {
			if !yield(k, v) {
				return
			}
		}
	}
}

// Keys returns an iterator over keys in m.
// The iteration order is not specified and is not guaranteed
// to be the same from one call to the next.

```

### Testing file

Then you could have a file `test_main.py` with your tests. It could live on the same Python package (the same directory with a `__init__.py` file):

``` hl_lines="5"
.
├── app
│   ├── __init__.py
│   ├── main.py
│   └── test_main.py
```

Because this file is in the same package, you can use relative imports to import the object `app` from the `main` module (`main.py`):

```Python hl_lines="3"

# Path Parameters and Numeric Validations

In the same way that you can declare more validations and metadata for query parameters with `Query`, you can declare the same type of validations and metadata for path parameters with `Path`.

## Import Path

First, import `Path` from `fastapi`, and import `Annotated`:

=== "Python 3.10+"

    ```Python hl_lines="1  3"
    {!> ../../../docs_src/path_params_numeric_validations/tutorial001_an_py310.py!}
    ```

// CHECK-NEXT:   [[HANDLE2:%.*]] = "tf.VarHandleOp"
// CHECK-NEXT:   [[KEY:%.*]], [[FUTURE:%.*]] = "tf.IfrtLoadVariable"([[HANDLE2]])
// CHECK-SAME:       used_by_host = false 
// CHECK-NEXT:   [[RES:%.*]] = "tf.IfrtCall"([[KEY]], %arg0) <{program_id = 6515870160938153680 : i64, variable_arg_indices = [0 : i32]}>
// CHECK-SAME:    : (tensor<!tf_type.string>, tensor<1x3xf32>) -> tensor<1x1xf32>
// CHECK-NEXT:    return [[RES]] : tensor<1x1xf32>
//
module {

    * So, by using FastAPI you are saving development time, bugs, lines of code, and you would probably get the same performance (or better) you would if you didn't use it (as you would have to implement it all in your code).

  func.return %dq : tensor<1x2xf32>
}

// -----

// CHECK-LABEL: @convert_quantfork_qdq_to_stablehlo_uniform_qdq
// CHECK-SAME: %[[ARG0:.*]]: tensor<1x3xf32>
// CHECK-SAME: %[[ARG1:.*]]: tensor<3x2xf32>
func.func @convert_quantfork_qdq_to_stablehlo_uniform_qdq(%arg0: tensor<1x3xf32>, %arg1: tensor<3x2xf32>) -> tensor<1x2xf32> {

* There can be **multiple processes** of the **same program** running at the same time.

If you check out the "task manager" or "system monitor" (or similar tools) in your operating system, you will be able to see many of those processes running.

  // Converts from read/write state that relates ops with the same parallel id
  // to a set of last accesses for use with other parallel ids. Reads/writes
  // between parallel ids are conservatively approximated as writes.
  absl::flat_hash_set<Operation*> GetLastWrites(ResourceId resource_id);

  // Sets the read/write state for ops within the same parallel id.
  void SetLastWrites(ResourceId resource_id,

// CHECK-LABEL: func @__forward_call_369
// CHECK-SAME: ([[in_chain:%.*]]: !tfrt.chain
// CHECK-SAME: [[arg1:%.*]]: !tfrt_fallback.tf_tensor {tf._user_specified_name = "inputs"},
// CHECK-SAME: [[arg2:%.*]]: !tfrt_fallback.tf_tensor, [[arg3:%.*]]: !tfrt_fallback.tf_tensor, [[arg4:%.*]]: !tfrt_fallback.tf_tensor, [[arg5:%.*]]: !tfrt_fallback.tf_tensor)
// CHECK-SAME: -> (!tfrt.chain
// CHECK: [[o1:%.*]] = tfrt_fallback_async.const_dense_tensor

// CHECK:  %cst = arith.constant
// CHECK:  %[[FQ:.*]] = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2)
// CHECK:  %[[R1:.*]] = "tf.Reshape"(%[[FQ]], %cst)
// CHECK-SAME: tensor<2x1xf32>
// CHECK:  %[[R2:.*]] = "tf.Reshape"(%[[FQ]], %cst)
// CHECK-SAME: tensor<2x1xf32>
}

// CHECK-LABEL: fakeQuantWithConv2D
func.func @fakeQuantWithConv2D(tensor<256x32x32x3xf32>) -> (tensor<256x8x7x16xf32>) {