%0 = "tf.Const"() {value = dense<[1, 2, 3, 4]> : tensor<4xui64>} : () -> tensor<4xui64>
  // CHECK: tfrt_fallback_async.const_dense_tensor  dense<1.000000e+00> : tensor<1xbf16>
  %1 = "tf.Const"() {device = "/device:CPU:0", value = dense<[1.0]> : tensor<1xbf16>} : () -> tensor<4xbf16>
  // CHECK: corert.executeop({{.*}}) "tf.Const"() {dtype = ui64, value = dense<[1, 2, 3, 4]> : tensor<4xui64>} : 1

    return %1: tensor<1x3xbf16>
  }

  pass_config.unfold_batch_matmul = toco_flags.unfold_batchmatmul();
  pass_config.lower_tensor_list_ops = toco_flags.lower_tensor_list_ops();
  // Disable the unfolding of the 16x16 TF::BatchMatMulOp to avoid the
  // conversion to an unsupported 16x16 TFL::FullyConnectedOp.
  if (toco_flags.inference_type() == toco::IODataType::QUANTIZED_INT16) {
    pass_config.unfold_batch_matmul = false;
  }

  func.return %0 : tensor<*xbf16>
}

func.func private @sum1(%arg0: tensor<*xbf16>, %arg1: tensor<*xbf16>) -> tensor<*xbf16> {
  %0 = "tf.AddV2"(%arg0, %arg1) : (tensor<*xbf16>, tensor<*xbf16>) -> tensor<*xbf16>
  func.return %0 : tensor<*xbf16>
}

// CHECK-LABEL: testXlaVariadicReduceToV2

				http.Redirect(w, r, u.String(), http.StatusTemporaryRedirect)
				return
			}
		}
		h.ServeHTTP(w, r)
	})
}

var redirectPrefixes = map[string]struct{}{
	"favicon-16x16.png": {},
	"favicon-32x32.png": {},
	"favicon-96x96.png": {},
	"index.html":        {},
	minioReservedBucket: {},
}

// Fetch redirect location if urlPath satisfies certain