)
    if use_kernel:
      model.einsum = model.einsum_with_kernel
      model_inputs = {'x': x}
    else:
      model.einsum = model.einsum_without_kernel
      model_inputs = {'x': x, 'y': y}

    saved_model_save.save(
        model, self._input_saved_model_path, signatures=model.einsum
    )

    signature_key = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY

    # via
    #   -r requirements.in
    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
opt-einsum==3.3.0 \
    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
    # via

// CHECK: Number of dequantize layers added: 1
}


// -----

module {
  func.func @float_einsum(%arg0: tensor<?x64x32xf32>, %arg1: tensor<32x2x16xf32>) -> (tensor<?x64x2x16xf32>) {
    %0 = "tf.Einsum"(%arg0, %arg1) {equation = "abc,cde->abde"} : (tensor<?x64x32xf32>, tensor<32x2x16xf32>) -> tensor<?x64x2x16xf32>
    func.return %0 : tensor<?x64x2x16xf32>
  }

// CHECK-LABEL: func @float_einsum

    # via
    #   -r requirements.in
    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
opt-einsum==3.3.0 \
    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
    # via

    # via
    #   -r requirements.in
    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
opt-einsum==3.3.0 \
    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
    # via

        "decompose_reduce_dataset.cc",
        "decompose_resource_ops_pass.cc",
        "device_attribute_to_launch.cc",
        "device_index_selector.cc",
        "drop_while_shape_invariant.cc",
        "einsum.cc",
        "executor_island_coarsening.cc",
        "executor_tpuv1_inline_tpu_island.cc",
        "executor_tpuv1_island_coarsening.cc",
        "executor_tpuv1_outline_tpu_island.cc",

// CHECK: %[[ARG0_CAST:.*]] = "tf.Cast"(%[[ARG0]]
// CHECK: %[[ARG0_REDUCE:.*]] = "tf.Einsum"(%[[ARG0_CAST]]
// CHECK-SAME: __tf_quant_created_einsum
// CHECK: %[[ARG0_ZP:.*]] = "tf.Mul"(%[[ARG0_REDUCE]]

// CHECK: %[[ARG1_CAST:.*]] = "tf.Cast"(%[[ARG1]]
// CHECK: %[[ARG1_REDUCE:.*]] = "tf.Einsum"({{.*}}, %[[ARG1_CAST]]
// CHECK-SAME: __tf_quant_created_einsum
// CHECK: %[[ARG1_ZP:.*]] = "tf.Mul"(%[[ARG1_REDUCE]]

// NEXT ID: 7
message UnitWiseQuantizationSpec {
  // Quantization unit granularity.
  // NEXT ID: 4
  message QuantizationUnit {
    // Type of the op, ex: Conv2D, MatMul, Einsum... The node_name field can
    // be omitted if it is intended to match all nodes with this type.
    string op_type = 1;
    // Name of the node. This field accepts re2 regex format. If the node name

  }

  void getDependentDialects(DialectRegistry& registry) const override {
    registry.insert<TF::TensorFlowDialect, arith::ArithDialect>();
  }

  void runOnOperation() override;
};

// Generate an einsum equation from the given DotDimensionNumber.
std::string CreateEinsumEquation(
    const xla::DotDimensionNumbers& dot_dimension_numbers, const int lhs_rank,
    const int rhs_rank) {
  // Prepare necessary indices.

std::unique_ptr<OperationPass<func::FuncOp>> CreateUnrollBatchMatMulPassPass();

// Optional pass which will map TF BatchMatMul to TF Einsum
std::unique_ptr<OperationPass<func::FuncOp>> CreateBatchMatMulToEinsumPass();

// Pass that transform Einsum to other TF Ops for the supported variants.
std::unique_ptr<OperationPass<func::FuncOp>> CreateTransformEinsumPass();

// Optimizes Tensorflow graph.