%graph = tf_executor.graph {
            %read0, %read0_control = tf_executor.island wraps "tf.ReadVariableOp"(%arg0) : (tensor<*x!tf_type.resource<tensor<32xf32>>>) -> tensor<32xf32>
            %assign0_control = tf_executor.island wraps "tf.AssignVariableOp"(%arg0, %arg1) : (tensor<*x!tf_type.resource<tensor<32xf32>>>, tensor<32xf32>) -> ()

  %12 = mhlo.clamp %9, %11, %1 : tensor<2xf32>
  %13 = mhlo.multiply %arg0, %12 : tensor<2xf32>
  %14 = mhlo.divide %13, %1 : tensor<2xf32>
  return %14 : tensor<2xf32>
}

// CHECK-LABEL:   func.func @hardswish(
// CHECK-SAME:                         %[[VAL_0:.*]]: tensor<2xf32>) -> tensor<*xf32> {
// CHECK:           %[[VAL_1:.*]] = "tfl.hard_swish"(%[[VAL_0]]) : (tensor<2xf32>) -> tensor<2xf32>

  %0 = "tf.Gather"(%arg0, %arg1) : (tensor<3x2xf32>, tensor<i32>) -> tensor<2xf32>
  func.return %0 : tensor<2xf32>

// CHECK-LABEL:gatherScalarIndices
// CHECK:  "tfl.gather"(%arg0, %arg1) <{axis = 0 : i32, batch_dims = 0 : i32}> : (tensor<3x2xf32>, tensor<i32>) -> tensor<2xf32>
}

func.func @gatherVectorIndices(%arg0 : tensor<2xf32>, %arg1 : tensor<3xi32>) -> tensor<3xf32> {

  %0 = "tfl.unidirectional_sequence_lstm"(%arg0,...

				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
// criteria. Some special names are considered to be

    } else {
      // Recurse on the subtypes in the variant/resource. Basically if the input
      // were:
      //   tensor<!tf_type.variant<tensor<?x8xf32>>>
      // and:
      //   tensor<!tf_type.variant<tensor<10x8xf32>>>
      // we'll try here to refine tensor<?x8xf32> with tensor<10x8xf32>.
      auto refined_subtype = mlir::cast<TensorType>(
          TypeMeet(lhs_element_type_with_subtype.GetSubtypes().front(),

from tensorflow.compiler.mlir.tensorflow.gen_mlir_passthrough_op import mlir_passthrough_op

mlir_module = '''python
func @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> {
   %add = "magic.op"(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>
   return %ret : tensor<10x10xf32>
}
'''

@tf.function
def foo(x, y):