// https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/batch-mat-mul
  int64_t x_row_dim = x_shape[x_shape.size() - 2];
  int64_t x_col_dim = x_shape[x_shape.size() - 1];
  int64_t y_row_dim = y_shape[y_shape.size() - 2];
  int64_t y_col_dim = y_shape[y_shape.size() - 1];
  int64_t out_row_dim = output_shape[output_shape.size() - 2];
  int64_t out_col_dim = output_shape[output_shape.size() - 1];

                            const std::vector<TensorShape>& x_shapes,
                            const OutputList& ys,
                            const std::vector<TensorShape>& y_shapes,
                            JAC_T* max_error) {
  if (xs.size() != x_shapes.size()) {
    return errors::InvalidArgument("xs(size ", xs.size(),
                                   ") and x_shapes(size ", x_shapes.size(),

    XlaLayoutPreference layout_preference) {
  xla::Shape xla_shape;
  TF_RETURN_IF_ERROR(
      tensorflow::TensorShapeToXLAShape(type, shape, &xla_shape));
  ApiConverter::StackHelper<XLA_Shape> se_shape(xla_shape);
  ApiConverter::StackHelper<XLA_Shape> tpu_shape;
  StatusHelper status;
  stream_executor::tpu::ExecutorApiFn()->XlaShapeToTpuShapeRepresentationFn(
      &se_shape.value, type, use_fast_memory, &tpu_shape.value,

          contracting_dims.add(c)
      x_signature = [
          None if c not in contracting_dims else x_shape[cidx]
          for cidx, c in enumerate(x_labels)
      ]
      y_signature = [
          None if c not in contracting_dims else y_shape[cidx]
          for cidx, c in enumerate(y_labels)
      ]
    return x_shape, y_shape, bias_shape, x_signature, y_signature

  def _create_einsum_model(
      self,

      has_bias: bool,
      use_kernel: bool,
  ):
    n = 5
    x_shape = [v if v is not None else n for v in shapes[0]]
    y_shape = [v if v is not None else n for v in shapes[1]]

    class MatmulModel(module.Module):

      def __init__(self, bias: Optional[core.Tensor]):
        self._bias = bias
        self._kernel = np.random.uniform(size=y_shape).astype('f4')
        self._min = (-0.8, -0.8, -0.9)

  std::vector<int64_t> in_shape{input_type.getShape().vec()};
  std::vector<int64_t> out_shape{output_type.getShape().vec()};

  // If the reshape changes the number of dimensions so it cannot be interpreted
  // as a transpose.
  if (in_shape.size() != out_shape.size()) {
    return false;
  }

  in_shape.erase(std::remove(in_shape.begin(), in_shape.end(), 1),
                 in_shape.end());

  mlir::FunctionType func_type = main_fn.getFunctionType();
  for (auto input_type : func_type.getInputs()) {
    tensorflow::TensorShape tensor_shape;
    xla::Shape xla_shape = xla::TypeToShape(input_type);
    TF_RETURN_IF_ERROR(tensorflow::TensorShape::BuildTensorShape(
        xla_shape.dimensions(), &tensor_shape));
    arg_shapes.emplace_back(tensor_shape);

    DataType dtype;
    TF_RETURN_IF_ERROR(ConvertToDataType(input_type, &dtype));

      [shape_determination_fns](
          const xla::Shape& xla_shape) -> absl::StatusOr<xla::Shape> {
    TensorShape shape;
    TF_RETURN_IF_ERROR(XLAShapeToTensorShape(xla_shape, &shape));
    TF_ASSIGN_OR_RETURN(DataType dtype, EncodePrimitiveTypeAsDataType(
                                            xla_shape.element_type()));
    auto layout_preference = shape_determination_fns.layout_preference_fn(

  // this can fail if we're not running on a TPU-enabled node.
  // TODO(kramm): Move this into a separate pass. See b/184944903
  xla::Shape old_shape = xla::TypeToShape(t);
  XLA_Shape old_shape_c = {};
  XLA_Shape new_shape_c = {};
  TfTpu_ExecutorApiFn *executor = stream_executor::tpu::ExecutorApiFn();
  if (!stream_executor::tpu::IsInitialized(executor)) {
    return failure();
  }

#include "tensorflow/c/experimental/saved_model/public/signature_def_param_list.h"
#include "tensorflow/c/experimental/saved_model/public/tensor_spec.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/c/tf_shape.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_tensor.h"
#include "tensorflow/core/lib/io/path.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/stringpiece.h"