16
          ],
          "name": "output",
          "quantization": {
          }
        }
      ],
      "inputs": [
        0,
        1
      ],
      "outputs": [
        2
      ],
      "operators": [
        {
          "inputs": [
            0,
            1
          ],
          "outputs": [
            2
          ],

                    println "strings: " + $.strings
                  }
                }
              }
              strings {
                add $.foo
              }
            }
        '''

        then:
        succeeds "printStrings"
        output.contains "strings: " + ["foo"]
    }

    def "inputs are fully configured when used in rules"() {

name: Latest Changes

on:
  pull_request_target:
    branches:
      - master
    types:
      - closed
  workflow_dispatch:
    inputs:
      number:
        description: PR number
        required: true
      debug_enabled:
        description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'
        required: false
        default: 'false'

jobs:
  latest-changes:

  // Tensors: Vector of tensors that back the TensorValue inputs
  // Inputs: Vector of inputs that are backed by tensors.
  mlir::LogicalResult PrepareKernelInputs(
      const llvm::SmallDenseSet<int>& required_consts,
      std::vector<tensorflow::XlaExpression>& expressions,
      std::vector<tensorflow::Tensor>& tensors,
      std::vector<tensorflow::TensorValue>& inputs);

name: FastAPI People

on:
  schedule:
    - cron: "0 14 1 * *"
  workflow_dispatch:
    inputs:
      debug_enabled:
        description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'
        required: false
        default: 'false'

jobs:
  fastapi-people:
    if: github.repository_owner == 'tiangolo'
    runs-on: ubuntu-latest
    steps:
      - name: Dump GitHub context

// RUN: odml-to-stablehlo-opt %s -tfl-parse-stablehlo-ops | FileCheck %s

module {
func.func @main(%arg0: tensor<2xi32>) -> tensor<2xi32> attributes {tf.entry_function = {inputs = "arg0", outputs = "tfl.custom1"}} {
  %0 = "tfl.custom"(%arg0, %arg0) {custom_code = "stablehlo.maximum", custom_option = #tfl<const_bytes : "0x00000100002401">} : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  func.return %0 : tensor<2xi32>
}
}

// CHECK:       module {

// RUN: odml-to-stablehlo-opt %s -tfl-parse-stablehlo-ops | FileCheck %s

module {
func.func @main(%arg0: tensor<2xi32>) -> tensor<2xi32> attributes {tf.entry_function = {inputs = "arg0", outputs = "tfl.custom1"}} {
  %0 = "tfl.custom"(%arg0, %arg0) {custom_code = "stablehlo.multiply", custom_option = #tfl<const_bytes : "0x00000100002401">} : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  func.return %0 : tensor<2xi32>
}
}

// CHECK:       module {

    %0 = "tf.VarHandleOp"() {_class = ["loc:@dense/kernel"], allowed_devices = [], container = "", device = "/job:worker/replica:0/task:1/device:CPU:0", shared_name = "var1"} : () -> tensor<!tf_type.resource<tensor<100x50xf32>>>
    %1 = "tf.ReadVariableOp"(%0) {device = ""} : (tensor<!tf_type.resource<tensor<100x50xf32>>>) -> tensor<100x50xf32>

/// to the graph associated with 'scope', which compute (and return in
/// 'grad_outputs') the symbolic partial derivatives of 'L' w.r.t 'inputs'.
Status AddSymbolicGradients(const Scope& scope,
                            const std::vector<Output>& outputs,
                            const std::vector<Output>& inputs,
                            const std::vector<Output>& grad_inputs,
                            std::vector<Output>* grad_outputs);

}

Status FakeSession::Run(
    const std::vector<std::pair<std::string, Tensor>>& inputs,
    const std::vector<std::string>& output_names,
    const std::vector<std::string>& target_nodes,
    std::vector<Tensor>* outputs) {
  tensorflow::RunMetadata run_metadata;
  return Run(tensorflow::RunOptions(), inputs, output_names, target_nodes,
             outputs, &run_metadata);
}

Status FakeSession::Run(