%0 = arith.constant dense<[3]> : tensor<1xi32>
      // expected-remark@above {{ID: 0}}
      %1 = "tf.RandomUniform"(%0) {device = "", seed = 3 : i64, seed2 = 5 : i64} : (tensor<1xi32>) -> tensor<3xf32>
      // expected-remark@above {{ID: 1}}
      // expected-remark@above {{Successors: {4}}}
      %2 = "tf.RandomUniform"(%0) {device = "", seed = 3 : i64, seed2 = 5 : i64} : (tensor<1xi32>) -> tensor<3xf32>

// does not alter the last dimension as FullyConnected will collapse all other
// dimensions into a single dimension. For example,
//
//   %shape = arith.constant dense<[1, 128, 64]> : tensor<3xi32>
//   %reshape = tfl.reshape(%input, %shape) // %input: tensor<128x64xf32>
//   %fc = tfl.fully_connected(%reshape, %filter, %bias)
//           {keep_num_dims = false, weights_format = "DEFAULT"}
//

  // The first Operand is assumed to be a TensorType around a variant with a
  // single subtype (e.g. tensor<!tf_type.variant<tensor<2xi32>>>). We will
  // copy this type to the first result, and copy the singular variant subtype
  // to the second result (tensor<2xi32>).
  DCOMMENT_OP(op, "Inferring shape for TensorListPopBackOp.");

  auto src_list_handle_t =