// through entry by entry.
    ArrayRef<int64_t> input_shape = input_type.getShape();
    int input_shape_size = input_shape.size();
    Shape slice_sizes(input_shape.begin(), input_shape.end());
    int slice_dimensions = slice_sizes.size();
    slice_sizes[slice_dimensions - 2] =
        std::min((int64_t)1, input_shape[input_shape_size - 2]);
    slice_sizes[slice_dimensions - 1] =

  // on `xla_call_module_context_` for details.
  std::vector<xla::Shape> input_shapes;
  input_shapes.reserve(op.getArgs().size());
  for (mlir::Type type : op.getArgs().getTypes()) {
    input_shapes.push_back(xla::TypeToShape(type));
  }

  absl::Status status = loader->RefineDynamicShapes(input_shapes);
  if (!status.ok()) {
    // Do not return false here.
    //

        file->getBuffer(), input_arrays, input_dtypes, input_shapes,
        output_arrays, control_output_arrays, graphdef_conversion_options,
        context);
  }
  return GraphdefToMlirTranslateFunction(file->getBuffer(), input_arrays,
                                         input_dtypes, input_shapes,
                                         output_arrays, control_output_arrays,

  int feature_group_cnt = 1;
  ShapedType input_shape =
      mlir::dyn_cast<ShapedType>(op->getOperand(0).getType());
  if (!input_shape) {
    return op->emitError(
        "Only input with known shape is supported for Uniform Quantized "
        "opset.");
  }

  if (op->getParentOfType<func::FuncOp>().getName().contains("depthwise_")) {
    feature_group_cnt = input_shape.getDimSize(3);
  }

  assert(input_shape.size() == stride.size());

  for (int i = 0, e = input_shape.size(); i < e; ++i) {
    if (ShapedType::isDynamic(input_shape[i])) continue;

    int64_t dim_i = input_shape[i];
    int64_t begin_i = begin[i];
    int64_t end_i = end[i];
    int64_t stride_i = stride[i];

    // [0]: mask for begin, [1]: mask for end
    int64_t masks[] = {begin_mask & (1 << i), end_mask & (1 << i)};

    %i32_min_filled = "tf.Fill" (%input_shape, %i32_min) : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
    %i32_max_filled = "tf.Fill" (%input_shape, %i32_max) : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
    %i32_act_max_f32_filled = "tf.Fill" (%input_shape, %i32_act_max_f32) : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>

  JitCompilationListener* listener() const { return listener_; }

  // Returns a test graph that will split into two XLA clusters (due to a node
  // with _XlaCompile = false).
  GraphDef GetTestGraph(const PartialTensorShape& input_shape);

  // Runs the graph using specified batch size both with and without XLA JIT
  // compilation. Returns an error if the results between the two do not match.
  Status ExecuteWithBatch(const GraphDef& graph, int batch);

    const GraphImportConfig& specs, absl::string_view debug_info_file,
    absl::string_view input_arrays, absl::string_view input_dtypes,
    absl::string_view input_shapes, absl::string_view output_arrays,
    absl::string_view control_output_arrays, llvm::SourceMgr* source_mgr,
    mlir::MLIRContext* context);

// Load Saved model (either v1 or v2) into MLIR.

                                     bool cifg) {
  SmallVector<int64_t, 2> input_shape{1, 2};
  SmallVector<int64_t, 2> weight_shape{3, 12};
  SmallVector<int64_t, 1> bias_shape{2};
  SmallVector<int64_t, 2> projection_shape{1, 2};
  SmallVector<int64_t, 1> layer_norm_scale{4};
  SmallVector<int64_t, 2> output_shape{1, 2};
  auto input_type = RankedTensorType::get(input_shape, builder->getF32Type());

    expand_op.setOperand(0, stb_op.getInput());
    // Calculate the shape for expand.
    auto input_shape =
        mlir::cast<ShapedType>(stb_op.getInput().getType()).getShape();
    SmallVector<int64_t, 4> expand_shape(input_shape.begin(),
                                         input_shape.end());
    expand_shape.insert(expand_shape.begin() + expand_axis, 1);

    auto expand_result_type = RankedTensorType::get(