// of intermediate tensors since importer doesn't guarantee to preserve tensor
// names except output tensors.
Location OpLoc(const OperatorT& op,
               const std::vector<std::unique_ptr<tflite::TensorT>>& tensors,
               Builder builder, Location base) {
  if (op.outputs.empty()) return base;

  llvm::SmallVector<Location, 4> locations;
  locations.reserve(op.outputs.size());

  }
}

}  // namespace

bool IsQuantized(const TensorT& tensor) {
  return (tensor.quantization != nullptr) &&
         !tensor.quantization->zero_point.empty();
}

// Returns the correct type for a quantized tensor.
// We have a special case for constants since they have a higher minimum value.
StatusOr<QuantizedType> GetQuantizedType(const TensorT& tensor, Builder builder,

// 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

  // 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));

// 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

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";
}

  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

#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"

namespace mlir {
namespace TFL {

bool IsQuantized(const tflite::TensorT& tensor);

absl::StatusOr<mlir::quant::QuantizedType> GetQuantizedType(
    const tflite::TensorT& tensor, mlir::Builder builder,
    bool is_constant = false, mlir::Type storage_type = {});

// 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

  }

  // Read all underlying tensors of the variables from the session.
  std::vector<Tensor> tensors;
  tensors.reserve(resource_tensors.size());
  for (const Tensor& resource_tensor : resource_tensors) {
    if (resource_tensor.dtype() != tensorflow::DT_RESOURCE) {
      tensors.push_back(resource_tensor);
      continue;
    }