And these same full-stack generators were the base of the [**FastAPI** Project Generators](project-generation.md){.internal-link target=_blank}.

!!! info
    Flask-apispec was created by the same Marshmallow developers.

!!! check "Inspired **FastAPI** to"
    Generate the OpenAPI schema automatically, from the same code that defines serialization and validation.

	//   This key matches all other keys with the same volumeName.
	// - volumeName exists, podName exists, nodeName empty
	//   This key matches with:
	//   - the same volumeName and podName
	//   - the same volumeName, but empty podName
	// - volumeName exists, podName empty, nodeName exists
	//   This key matches with:
	//   - the same volumeName and nodeName
	//   - the same volumeName but empty nodeName

// RUN: tf-opt %s -pass-pipeline='builtin.module(func.func(canonicalize))' | FileCheck %s


// Test single graph with no outputs and one island is folded away.
// CHECK-LABEL: func @graph_with_no_outputs
// CHECK-SAME: (%[[ARG_0:[a-z0-9]*]]: tensor<i1>)
func.func @graph_with_no_outputs(%arg0 : tensor<i1>) {
  tf_executor.graph {
    %1:2 = tf_executor.island {
      %3 = "tf.opA"(%arg0) : (tensor<i1>) -> tensor<i1>

=== "Python 3.10+"

    You can use the same builtin types as generics (with square brackets and types inside):

    * `list`
    * `tuple`
    * `set`
    * `dict`

    And the same as with Python 3.8, from the `typing` module:

    * `Union`
    * `Optional` (the same as with Python 3.8)
    * ...and others.

// CHECK: %[[tconv:.*]] = "tfl.transpose_conv"(%arg1, %[[dq_w:.*]], %arg0, %[[b:.*]]) <{
// CHECK-NOT: asymmetric_quantize_inputs = true
// CHECK-SAME: padding = "SAME"
// CHECK: return %[[tconv:.*]]

// PerTensor-DAG: %[[w:.*]] = arith.constant dense<1.270000e+02> : tensor<1x32x42x128xf32>
// PerTensor-DAG: %[[b:.*]]= arith.constant dense<0.000000e+00> : tensor<1x32x42x128xf32>

    return %0 : tensor<2x3x1x3xf32>
  }
}

// -----

// Tests if reduce_window op following quantized same-scale op is quantized.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1722 : i32}, tf_saved_model.semantics} {
  // CHECK-LABEL: main_00
  // CHECK-SAME: %[[ARG0:.*]]: tensor<2x3x1x1024xf32>

// Transpose ops are inserted to the activation and output of
// `stablehlo.convolution`. The weight constants is transposed.

// CHECK-LABEL: nchw_conv_with_nonconst_bias_add
// CHECK-SAME: %[[ARG_0:.+]]: tensor<1x2x5x5xf32>
// CHECK-SAME: %[[ARG_1:.+]]: tensor<1x4x5x5xf32>
func.func @nchw_conv_with_nonconst_bias_add(%arg0: tensor<1x2x5x5xf32>, %arg1: tensor<1x4x5x5xf32>) -> tensor<1x4x5x5xf32> {

    return %5 : tensor<*xi64>
  }

// Check that HashTable op is lifted.
// CHECK: func.func private @serving_default
// CHECK-SAME: (%arg0: tensor<?x!tf_type.string>, %arg1: tensor<!tf_type.resource>) -> tensor<*xi64>
// CHECK-SAME: tf.entry_function = {control_outputs = "", inputs = "input_vocabs:0,hash_table_1:0", outputs = "FakeQuantWithMinMaxArgs_2:0"}
// CHECK: "tf.LookupTableSizeV2"(%arg1)

    {!> ../../../docs_src/response_model/tutorial002.py!}
    ```

Now, whenever a browser is creating a user with a password, the API will return the same password in the response.

In this case, it might not be a problem, because it's the same user sending the password.

But if we use the same model for another *path operation*, we could be sending our user's passwords to every client.

!!! danger

  %0 = "tfl.conv_2d"(%arg0, %arg1, %arg2) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x112x80x128xf32>, tensor<128x3x3x128xf32>, tensor<128xf32>)...