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++;
      }
    });

stderr '^go: m@latest: malformed module path "m": missing dot in first path element$'

# download without arguments updates go.mod and go.sum after loading the
# build list, but does not save sums for downloaded zips.
cd update
cp go.mod.orig go.mod
! exists go.sum
go mod download
cmp go.mod.update go.mod
cmp go.sum.update go.sum
cp go.mod.orig go.mod
rm go.sum

    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

This is for example the case when between projects you share a _directory of files_, but there's no way you can publish a _directory_ directly on a Maven repository (only packaged things like jars or zips).
Look at the link:{javadocPath}/org/gradle/api/component/ConfigurationVariantDetails.html[ConfigurationVariantDetails] class for details about how to skip publication of a particular variant.

// CHECK-NEXT: return %[[BIASADD]] : tensor<?x?x?x3xf32>

// -----

func.func @lower_einsum(%arg0: tensor<3x4x5xf32>, %arg1: tensor<3x5x6xf32>) -> tensor<3x4x6xf32> {
  %0 = "tf.Einsum"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", equation = "ijk,ikm->ijm"}: (tensor<3x4x5xf32>, tensor<3x5x6xf32>) -> tensor<3x4x6xf32>
  func.return %0 : tensor<3x4x6xf32>
}
// CHECK-LABEL: lower_einsum