// There should be 3 tensors: input, output, and BroadcastTo/shape.
  EXPECT_THAT(subgraph->tensors, SizeIs(3));

  // Input Tensor
  EXPECT_THAT(subgraph->tensors[0]->type, Eq(tensor_type_));
  EXPECT_THAT(subgraph->tensors[0]->name, Eq("input_1"));
  EXPECT_THAT(subgraph->tensors[0]->quantization->scale, SizeIs(1));
  EXPECT_THAT(subgraph->tensors[0]->quantization->zero_point, SizeIs(1));

// in the plugin for Training. The API takes in the context with indices for the
// input and value tensors. It also accepts the copy callback provided by
// pluggable vendor to do the copying of the tensors and the update callback to
// apply the arithmetic operation. The caller takes ownership of the `source`,
// `dest`, `tensor` and `value` tensors and is responsible for freeing them with

                const OutputList& tensors, std::vector<Tensor>* out);

/// Computes the output 'tensor', returning the resulting tensor in 'out'.
void GetTensor(const Scope& scope, Output tensor, Tensor* out);

// Computes the output 'tensor', returning the resulting tensor in 'out'.
// assign_vars are extra outputs that should be run
// e.g. to assign values to variables.

  let summary = "Loads a restored variable tensor as a tensor future";

  let description = [{
    This op loads a restored variable tensor as a tensor future. It is a
    replacement of `tf.ReadVariableOp`.

    This op returns a scalar string tensor containing the restored variable name, which 
    is composed from `container_name` and `shared_name` from a `var_handle` and can be

  int num_retvals = tensors.size();
  return op_ptr->Execute(tensors, &num_retvals);
}

// Op: SaveV2()
// Summary: Saves tensors in V2 checkpoint format.
//
// Description:
//   By default, saves the named tensors in full.  If the caller wishes to save
//   specific slices of full tensors, "shape_and_slices" should be non-empty
//   strings and correspondingly well-formed.

namespace tensorflow {
namespace test {

void GetTensors(const Scope& scope, OutputList tensors,
                std::vector<Tensor>* out) {
  ClientSession session(scope);
  TF_CHECK_OK(session.Run(tensors, out));
}

void GetTensor(const Scope& scope, Output tensor, Tensor* out) {
  std::vector<Tensor> outputs;
  GetTensors(scope, {std::move(tensor)}, &outputs);
  *out = outputs[0];
}

// as tensor names may have changed via MLIR constant folding process.
bool ExpectEqualTensor(const Tensor* tensor, const Tensor* expected_tensor) {
  return (tensor->is_variable() == expected_tensor->is_variable()) &&
         (GetAsVector(tensor->shape()) ==
          GetAsVector(expected_tensor->shape()));
}

// Finds the match of the quantized tensor from the possible tensors. Each

// model.
table SubGraph {
  // A list of all tensors used in this subgraph.
  tensors:[Tensor];

  // Indices of the tensors that are inputs into this subgraph. Note this is
  // the list of non-static tensors that feed into the subgraph for inference.
  inputs:[int];

  // Indices of the tensors that are outputs out of this subgraph. Note this is
  // the list of output tensors that are considered the product of the

    for (const int itensor : operations_[ioperation].tensors) {
      if (const auto& tensor = tensors_[itensor];
          tensor.first_use() >= begin && tensor.first_use() < end &&
          tensor.last_use() > peak_loc) {
        for (const int ioperation : tensor.operations) {
          // We return the earliest dependence on any output tensor.
          if (ioperation > peak_loc && ioperation < best) {

def PromiseOp: TensorflowMlrt_Op<"promise", []> {
  let summary = "Set a tensor in a promise";

  let description = [{
    Set a tensor in a promise.

    $promise: A value of type !mlrt.promise. The underlying value must be a tensorflow tensor.
    $tensor: A tensorflow tensor.
  }];

  let arguments = (ins
    MlrtPromiseType:$promise,
    TFTensorType:$tensor
  );

  let assemblyFormat = "operands attr-dict";
}