// Checks if the data format is "NDHWC".
def IsDataFormatNDHWC : ConstantAttr<TF_ConvnetDataFormatAttr, "\"NDHWC\"">;

// Checks if the op is constant op.
def IsConstTensor :  Constraint<CPred<"dyn_cast_or_null<TF::ConstOp>($0.getDefiningOp())">>;

// Checks if the element value has a float type.
def IsFloatElementsAttr : ElementsAttrBase<
  CPred<"$_self.isa<ElementsAttr>() && "

}

TEST_F(LegalizationOpConfigTest, ChecksDynamicPadderOps) {
  EXPECT_TRUE(
      IsDynamicPadderOp(TypeID::get<TF::XlaSetDynamicDimensionSizeOp>()));
  EXPECT_FALSE(IsDynamicPadderOp(TypeID::get<TF::ConstOp>()));
}

// This test is kind of odd. We go through all the Tensorflow types and check
// whether they are legalized with MLIR, TF2XLA, or both. Ideally the sets are

  // Enqueues a memcpy operation onto stream, with a host destination location
  // `host_dst` and a device memory source, with target size `size`.
  void (*memcpy_dtoh)(const SP_Device* device, SP_Stream stream, void* host_dst,
                      const SP_DeviceMemoryBase* device_src, uint64_t size,
                      TF_Status* status);

  // Enqueues a memcpy operation onto stream, with a device destination

## Why is MinIO cloud-native?

        }
      }

      // Replace the arg with a tf.Const op in the function body.
      builder.setInsertionPointToStart(&func.getBody().front());
      auto const_op = builder.create<TF::ConstOp>(global_tensor.getLoc(),
                                                  *global_tensor.getValue());
      args_to_erase.set(val.getArgNumber());
      for (auto read_op : read_variable_ops_to_erase) {

        // If the script file is known, determine its type
        val scriptType = scriptFile?.let {
            KotlinScriptTypeMatch.forFile(it)?.scriptType
        }

        // We also add the "buildSrc" sources onto the source path.
        val projectBuildSrcRoots = when (scriptType) {
            KotlinScriptType.INIT, KotlinScriptType.SETTINGS -> emptyList()
            else -> buildSrcRootsOf(projectDir)
        }

     * and gives an attacker the ability to
     * <a href="https://max.computer/blog/how-to-take-over-the-computer-of-any-java-or-clojure-or-scala-developer/">serve malicious executable code onto the system.</a>
     * </b>
     * <p>
     * See also:

    $mlir_module is a serialized MLIR module with a `main` function that contains target computation.
    $metadata is a serialized TPUCompileMetadataProto describing the shapes and types of the inputs to the computation, as well as a mapping onto the TPU pod topology.
    $constant_operand_indices are the indices of the inputs that are constant to the TPU program (e.g. weights in inference), the rest of the inputs are input tensors.

        RankedTensorType::get(static_shape.size(), rewriter.getIntegerType(64));
    auto static_shape_attr =
        mlir::DenseIntElementsAttr::get(static_shape_type, static_shape);
    return rewriter.create<TF::ConstOp>(loc, static_shape_attr).getOutput();
  }

  // If the shape is not static, create a new ShapeOp.
  BoolAttr false_attr = rewriter.getBoolAttr(false);
  return rewriter
      .create<TF::ShapeOp>(loc, input,

	defer close(c4)

	for i := 0; i < n; i++ {
		select {
		case c1 <- i:
		case c2 <- i:
		case c3 <- i:
		case c4 <- i:
		}
	}
}

// mux receives the values from sender and forwards them onto another channel.
// It would be simpler to just have sender's four cases all be the same
// channel, but this doesn't actually trigger the bug.
func mux(out chan<- int, in <-chan int, done chan<- bool) {
	for v := range in {