}
// CHECK-LABEL: func.func @conv_srq
// CHECK-SAME: (%[[ARG_0:.+]]: tensor<1x5x5x2x!quant.uniform<i8:f32, 2.000000e+00>>) -> tensor<1x4x4x4x!quant.uniform<i8:f32, 8.000000e+00:-128>>
// CHECK-DAG: %[[CONST_0:.+]] = "tfl.pseudo_const"() <{value = dense<{{\[\[0, 0\], \[1, 1\], \[1, 1\], \[0, 0\]\]}}> : tensor<4x2xi32>}> : () -> tensor<4x2xi32>

  %0 = "tf.Relu"(%arg0) : (tensor<1xi32>) -> tensor<1xi32>
  func.return %0: tensor<1xi32>

// CHECK-LABEL: testReluI32
// CHECK:  %[[CONST_0:.*]] = arith.constant dense<0> : tensor<i32>
// CHECK:  %[[RES0:.*]] = "tfl.maximum"(%arg0, %[[CONST_0]]) : (tensor<1xi32>, tensor<i32>) -> tensor<1xi32>
// CHECK:  return %[[RES0]] : tensor<1xi32>
}

func.func @testReluI64(%arg0: tensor<1xi64>) -> tensor<1xi64> {

    ```mlir
      %old_val = "tf.ReadVariableOp"(%buffer)
      %old_size = "tf.ReadVariableOp"(%size)
      %offsets = "tf.ConcatV2"(%old_size, %other_dims_0s, %const0)
      %new_val = "tf.XlaDynamicUpdateSlice"(%old_val, %push_val, %offsets)
      "tf.AssignVariableOp"(%buffer, %new_val)
      %new_size = "tf.AddV2"(%old_size, %const1)
      "tf.AssignVariableOp"(%size, %new_size)

  // CHECK-DAG: %[[CONST0:.*]] = "tf.Const"() <{value = dense<[2.000000e+00, 3.000000e+00, 1.000000e+00]>
  // CHECK: %[[CONCAT:.*]] = "tf.ConcatV2"(%arg0, %arg1, %arg2, %[[CONST]]) {device = "/job:localhost/replica:0/task:0/device:GPU:0"}
  // CHECK: "tf.Mul"(%[[CONCAT]], %[[CONST0]]) {device = "/job:localhost/replica:0/task:0/device:GPU:0"}