let description = [{
Performs a batched matrix multiplication on the inputs. Follows the
conventions of TensorFlow BatchMatMulV2, with support for unknown dimensions
in the batch dimensions and broadcasting.

    Inputs:
      `inputs[0]`: required: input LHS
      `inputs[1]`: required: input RHS
      `adjoint_lhs`: optional: Transpose LHS (default false)

    }
    ImmutableList<SettableFuture<Long>> inputs = builder.build();
    ImmutableList<ListenableFuture<Long>> delegates = inCompletionOrder(inputs);

    for (ListenableFuture<?> delegate : delegates) {
      delegate.cancel(true);
    }

    for (ListenableFuture<?> input : inputs) {
      assertTrue(input.isDone());
    }
  }

    }
    ImmutableList<SettableFuture<Long>> inputs = builder.build();
    ImmutableList<ListenableFuture<Long>> delegates = inCompletionOrder(inputs);

    for (ListenableFuture<?> delegate : delegates) {
      delegate.cancel(true);
    }

    for (ListenableFuture<?> input : inputs) {
      assertTrue(input.isDone());
    }
  }

                    def input = inputArtifact.get().asFile
                    def output = outputs.file(input.name + ".\${targetColor}")
                    assert output.parentFile.directory && output.parentFile.list().length == 0
                    println "Transforming \${input.name} to \${output.name}"
                    println "Input exists: \${input.exists()}"

	}
	// iterate through the above set of inputs and upload the object.
	for i, input := range putObjectInputs {
		// uploading the object.
		_, err := obj.PutObject(context.Background(), input.bucketName, input.objectName, mustGetPutObjReader(t, bytes.NewReader(input.textData), input.contentLength, input.metaData[""], ""), ObjectOptions{UserDefined: input.metaData})
		// if object upload fails stop the test.
		if err != nil {

      // Shape inference should not change the element type.
      if (HasCompatibleElementTypes(input.getType(), result.getType()) &&
          meet == input.getType()) {
        arg_op.set(input);
      } else {
        OpBuilder b(return_op.getOperation());
        auto new_cast_op = InsertCast(b, return_op.getLoc(), meet, input);
        if (!new_cast_op) return failure();
        arg_op.set(new_cast_op->getResult(0));

    module {
      func @while_body {
        %input = "tf.IteratorGetNext"(...) {device = "/CPU:0"}: -> tensor<2x224x224x3xf32>
        %device_launch = "tf_device.cluster_func"(%input,...) {func = @_func,...)
        return ...
      }
      func @_func(%input: tensor<2x224x224x3xf32>, %filter: tensor<7x7x3x64xf32>) {

          dtypes.float32, shape=[], name='input'
      )
      q_input = array_ops.fake_quant_with_min_max_args(
          input_tensor, min=-0.1, max=0.2, num_bits=8, narrow_range=False
      )
      sqrt = math_ops.sqrt(q_input, name='sqrt')
      identity = array_ops.identity(sqrt, name='output')

      input_map = {'input': input_tensor}
      output_map = {'sqrt': identity}

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

// Unary math ops
// -----

// CHECK-LABEL: testCos
func.func @testCos(tensor<? x f32>) -> tensor<? x f32> {
^bb0(%arg0: tensor<? x f32>):
  // CHECK: "tfl.cos"(%arg0)
  %0 = "tfl.cos"(%arg0): (tensor<? x f32>) -> tensor<? x f32>
  func.return %0 : tensor<? x f32>
}

// -----

// test invalid Cos input

    protected A doBackward(B b) {
      return convert(bimap.inverse(), b);
    }

    private static <X, Y> Y convert(BiMap<X, Y> bimap, X input) {
      Y output = bimap.get(input);
      checkArgument(output != null, "No non-null mapping present for input: %s", input);
      return output;
    }

    @Override
    public boolean equals(@CheckForNull Object object) {