### Format

The format for ADR should follow this template:

```markdown
# ADR-000X - Title

## Date

20YY-MM-DD

## Context

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna
aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.

                    "\""# 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.

# 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"

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/core/util/matmul_bcast.h"

namespace mlir {
namespace TF {

namespace {

// Replace TF BatchMatMul by TF Einsum op
template <typename BatchMatMulOpType>
class ConvertTFBatchMatMulToEinsumOp
    : public OpRewritePattern<BatchMatMulOpType> {
  using OpRewritePattern<BatchMatMulOpType>::OpRewritePattern;

bool IsInLiftedFunc(Operation* op);

// Checks if the op is inside a StableHLO op with region.
// If the given op pointer is a nullptr, returns false.
bool IsInStableHloOpRegion(Operation* op);

// Checks if a given einsum op is supported for XlaDotV2 quantization.
bool IsEinsumSupportedByXlaDotV2(StringAttr equation_attr);

// Gets the quantization method from `op`. It is retrieved from the
// `kQuantizationMethodAttr` string attribute. Returns

  %0 = mhlo.constant dense<(1.000000e+00,-1.000000e+00)> : tensor<128x32x4xcomplex<f32>>
  %1 = mhlo.constant dense<(1.000000e+00,1.000000e+00)> : tensor<8x64x128xcomplex<f32>>
  %2 = "mhlo.einsum"(%1, %0) <{einsum_config = "abc,cde->abde"}> : (tensor<8x64x128xcomplex<f32>>, tensor<128x32x4xcomplex<f32>>) -> tensor<8x64x32x4xcomplex<f32>>
  return %2 : tensor<8x64x32x4xcomplex<f32>>
}

// -----

          function_name.contains("and_bias")) {
        spec->biases_params[2] = {{0, 1},
                                  quant::GetUniformQuantizedTypeForBias};
      }
    } else if (function_name.contains("einsum")) {
      spec->coeff_op_quant_dim[1] = -1;
      if (function_name.contains("with_bias")) {
        spec->biases_params[2] = {{0, 1},
                                  quant::GetUniformQuantizedTypeForBias};
      }

    }

    public GradleVersion getVersion() {
        return GradleVersion.current();
    }

    /**
     * The timestamped version used in the docs and the bin and all zips. This should be different to {@link GradleVersion#getVersion()}.
     */
    public GradleVersion getDistZipVersion() {

	if len(args) != 0 {
		// NOTE(rsc): Could take a module pattern.
		base.Fatalf("go: verify takes no arguments")
	}
	modload.ForceUseModules = true
	modload.RootMode = modload.NeedRoot

	// Only verify up to GOMAXPROCS zips at once.
	type token struct{}
	sem := make(chan token, runtime.GOMAXPROCS(0))

	mg, err := modload.LoadModGraph(ctx, "")
	if err != nil {
		base.Fatal(err)
	}
	mods := mg.BuildList()

                }

            repositories {
                ivy {
                    // Url of Android Studio archive
                    url = uri("https://redirector.gvt1.com/edgedl/android/studio/ide-zips")
                    patternLayout {
                        artifact("[revision]/[artifact]-[revision]-[ext]")
                    }
                    metadataSources { artifact() }
                    content {