]
      )
      def einsum_without_kernel(
          self, x: core.Tensor, y: core.Tensor
      ) -> Mapping[str, core.Tensor]:
        return self._einsum(x, y)

      def _einsum(self, x, y):

        out = tensorflow.einsum(equation, x, y)
        if self._bias is not None:
          out = nn_ops.bias_add(out, self._bias)
        return {'output': out}

    model = EinsumModel()

              ),
          ]
      )
      def einsum_without_kernel(
          self, x: core.Tensor, y: core.Tensor
      ) -> Mapping[str, core.Tensor]:
        return self._einsum(x, y)

      def _einsum(self, x, y):
        if is_qat_model:
          x = array_ops.fake_quant_with_min_max_vars(
              x,
              min=ops.convert_to_tensor(self._min[0]),

// RUN: tf-opt -split-input-file -verify-diagnostics -tf-einsum %s | FileCheck %s

func.func @unary_einsum_reduce_sum_transpose(%arg0: tensor<3x4x5x6xf32>) -> tensor<3x5x4xf32> {
  %0 = "tf.Einsum"(%arg0) {T = "tfdtype$DT_FLOAT", equation = "...gse->...sg"}: (tensor<3x4x5x6xf32>) -> tensor<3x5x4xf32>
  func.return %0 : tensor<3x5x4xf32>
  // CHECK-LABEL: unary_einsum_reduce_sum_transpose
  // CHECK-DAG: %[[cst:.*]] = arith.constant dense<3> : tensor<1xi32>

}

struct EinsumDimensionNumbers {
  // Each field contains the list of dimensions appearing only in the specifed
  // arguments of the einsum op with natural ordering. For example `rhs_out`
  // contains the dimensions appearing in the RHS and the OUTPUT of the einsum
  // but not in the LHS.
  std::vector<int64_t> lhs;
  std::vector<int64_t> rhs;
  std::vector<std::tuple<int64_t, int64_t>> lhs_rhs;

// RUN: tf-opt %s -tf-batch-matmul-to-tf-einsum | FileCheck %s

func.func @test_batch_matmul_to_einsum(%arg0: tensor<1x2x3xf32>, %arg1: tensor<3x4xf32>) -> tensor<1x2x4xf32> {
  // CHECK-LABEL: test_batch_matmul_to_einsum
  // CHECK: "tf.Einsum"(%arg0, %arg1) <{equation = "...mk,...kn->...mn"}> : (tensor<1x2x3xf32>, tensor<3x4xf32>) -> tensor<1x2x4xf32>

  func.return %0 : tensor<?x2x4x5xf32>
}

// CHECK: func @xla_dot_v2
// CHECK: %[[einsum:.*]] = "tf.Einsum"(%arg0, %arg1) <{equation = "abc,cde->abde"}> : (tensor<?x2x3xf32>, tensor<3x4x5xf32>) -> tensor<?x2x4x5xf32>
// CHECK: return %[[einsum]] : tensor<?x2x4x5xf32>

// -----

// dimension_numbers: {
//   offset_dims: 0
//   collapsed_slice_dims: 1
//   start_index_map: 1

                    "\""# func_name #"\", $0...)", returns>;

// Add the second argument to the first argument, which is expected to be an
// argument list.
// bias(einsum(inputs), bias) --> einsum_with_bias(AppendToVector(inputs, bias))
// Since inputs is a vector in case of einsum, we cannot use ArgumentList here.
def AppendToVector : NativeCodeCall<"AppendToVector($0, $1)">;

// The list of arguments of the composite function.

  [], (addBenefit 10)>;

// Converts inlined Einsum pattern to TF XlaDotV2 op.
def ConvertTFEinsumToXLADotV2Op : Pat<
  (TF_EinsumOp:$einsum
    $args, $equation),
  (CreateXlaDotV2OpFromTfEinsumOp
    $equation, $args, $einsum),
  [(IsInt32ElementType $einsum),
   // Constraint to check:
   // 1. The einsum has two inputs and one output.
   // 2. The einsum is not created by the convert function itself.

def IsPrecisionEmpty :
  Constraint<CPred<"IsPrecisionEmpty($0)">>;

// Creates Einsum Op from XlaDotV2 Op by generating equation.
def CreateEinsumOpFromXlaDotV2Op : NativeCodeCall<
  "CreateEinsumOpFromXlaDotV2Op($_builder, $_loc, $0...)">;

// Convert XlaDotV2 Op to Einsum Op with above two functions.
def ConvertXlaDotV2OpToEinsumOp : Pat<

# REQUIREMENTS_FILE=jax.requirements.txt


setuptools
wheel
cloudpickle
colorama>=0.4.4
matplotlib
pillow>=9.1.0
rich
absl-py
portpicker
six
opt-einsum
auditwheel
typing_extensions
importlib_metadata>=4.6
numpy==1.26.0;python_version=="3.12"
numpy==1.23.4;python_version=="3.11"
numpy==1.22.4;python_version<"3.11"
scipy==1.11.2;python_version=="3.12"