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.

        testDir = temporaryFolder.testDirectory
        distributionDir = new TestFile(testDir, 'someDistPath')
        gradleHomeDir = new TestFile(distributionDir, 'gradle-0.9')
        zipStore = new File(testDir, 'zips')
        zipDestination = new TestFile(zipStore, zipFileName)
        install = new Install(new Logger(true), download, pathAssembler)
    }

    private void initConfiguration() {

    %16 = stablehlo.broadcast_in_dim %6, dims = [0, 1, 2] : (tensor<1x1x1xi32>) -> tensor<8x16x4xi32>
    %17 = stablehlo.subtract %15, %16 : tensor<8x16x4xi32>  // q2 - z2
    // Corresponds to einsum expression: b i j, b j d -> b i d
    %18 = stablehlo.dot_general %14, %17, batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<8x16x16xi32>, tensor<8x16x4xi32>) -> tensor<8x16x4xi32>

SmallVector<Value> AppendToVector(const ArrayRef<Value> arguments,
                                  Value append) {
  SmallVector<Value> ret(arguments);
  ret.push_back(append);
  return ret;
}

// Check if the given einsum equation is supported by XlaDotV2.
// Conditions:
// 1. Two inputs & one output.
// 2. No ... in the equation.
// 3. Batch dimensions should be the same, or only the left equation should have

          quantize_func_count++;
        } else if (func_name.starts_with("dequantize_i")) {
          dequantize_func_count++;
        }
      } else if (auto einsum = llvm::isa<TF::EinsumOp>(op)) {
        if (IsInCompsiteFunction(op)) return;
        // Leftover Einsum ops are always non-quantized.
        auto op_name = op->getName().stripDialect();
        func_count_map[op_name].num_float++;
      }
    });

    MLIR pipeline in the future.
  }];
  let constructor = "TF::CreateGpuOpFusionPass()";
}

def BatchMatMulToEinsumPass : Pass<"tf-batch-matmul-to-tf-einsum", "mlir::func::FuncOp"> {
  let summary = "Replace TF BatchMatMul op by TF Einsum op.";
  let constructor = "TF::CreateBatchMatMulToEinsumPass()";
}

			},
			expectedError: true,
		},

		{
			description: "bad arg intermixed",
			policyOptions: map[string]string{
				FullPCPUsOnlyOption: "1",
				"badvalue2":         "lorem ipsum",
			},
			expectedError: true,
		},
	}

	for _, testCase := range testCases {
		t.Run(testCase.description, func(t *testing.T) {
			opts, err := NewStaticPolicyOptions(testCase.policyOptions)

consumablePlatformVariant("runtimePlatform", listOf(coreRuntimeOnly, pluginsRuntimeOnly))

// A lifecycle task to build all the distribution zips for publishing
val buildDists by tasks.registering

configureDistribution("normalized", binDistributionSpec(), buildDists, true)
configureDistribution("bin", binDistributionSpec(), buildDists)

TensorFlow graph export by making transformation such as replacing or
removing MLIR or XLA specific attributes that are not legal in TensorFlow
graph.
### `-tf-batch-matmul-to-tf-einsum`

_Replace TF BatchMatMul op by TF Einsum op._

### `-tf-broadcast-fold`

_Fold explicit broadcasts into the following operations if they support implicit broadcasting on their operand._

type IfaceAny struct {
	T1  T1
	Any any `xml:",any"`
	T2  T2
}

type Generic[T any] struct {
	X T
}

var (
	nameAttr     = "Sarah"
	ageAttr      = uint(12)
	contentsAttr = "lorem ipsum"
	empty        = ""
)

// Unless explicitly stated as such (or *Plain), all of the
// tests below are two-way tests. When introducing new tests,
// please try to make them two-way as well to ensure that