// expected-error @+1 {{requires offset to be a 1D float tensor}}

// CHECK:           }) : (tensor<1x?xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x0xf32>, tensor<1x3xf32>, tensor<1x3xf32>, tensor<1x3xf32>, tensor<1x3xf32>, none, none, none, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xf32>, tensor<3x1xf32>, tensor<3xf32>, tensor<1x3xf32>, tensor<1x1xf32>, none, none, none, none) -> tensor<1x3xf32>
// CHECK:           [[VAL_50:%.*]] = tensor.cast [[VAL_51:%.*]] : tensor<1x3xf32> to tensor<1x?xf32>

}

func.func @atan2(%arg0: tensor<8xf32>, %arg1: tensor<8xf32>) -> tensor<8xf32> {
  %0 = "tf.Atan2"(%arg0, %arg1) : (tensor<8xf32>, tensor<8xf32>) -> tensor<8xf32>
  func.return %0 : tensor<8xf32>

// CHECK-LABEL: atan2
// CHECK: %[[RES0:.*]] = "tfl.atan2"(%arg0, %arg1) : (tensor<8xf32>, tensor<8xf32>) -> tensor<8xf32>
// CHECK:  return %[[RES0]] : tensor<8xf32>
}

    }) {is_stateless = false} : (tensor<i32>, tensor<!tf_type.variant<tensor<?x1xf32>>>) -> (tensor<i32>, tensor<!tf_type.variant<tensor<?x1xf32>>>)
    %elem_1 = "tf._SomeOtherOp"() : () -> tensor<8x1xf32>
    %tl_set_item = "tf.TensorListSetItem"(%while#1, %one, %elem_1) : (tensor<!tf_type.variant<tensor<?x1xf32>>>, tensor<i32>, tensor<8x1xf32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>>
    func.return
  }

  func.return %24 : tensor<1x4xf32>
}

    "tf.AssignVariableOp"(%arg0, %partitioned_output#0) : (tensor<!tf_type.resource<tensor<3x2xf32>>>, tensor<3x2xf32>) -> ()
    "tf.AssignVariableOp"(%arg1, %partitioned_output#1) : (tensor<!tf_type.resource<tensor<3x2xf32>>>, tensor<3x2xf32>) -> ()
    func.return
  }
  func.func @computation(%arg0: tensor<3x4xf32>) -> tensor<3x4xf32> {
    func.return %arg0: tensor<3x4xf32>
  }
}

// -----

    ```mlir
      %0 = "tf.Const"() {value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
      %1 = "tf.Const"() {device = "", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
      %2 = "tf.Const"() {device = "baz", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
    ```

    then running this pass with 'default-device=foobar', we get:

    ```mlir

    } 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(),

func.func @testSubOfZero(%arg0: tensor<?x1xf32>, %arg1: tensor<4x1xf32>) -> (tensor<?x1xf32>, tensor<4x1xf32>) {
  %0 = "tf.Const"() {value = dense<0.0> : tensor<f32>} : () -> tensor<f32>
  %1 = "tf.Sub"(%arg0, %0) : (tensor<?x1xf32>, tensor<f32>) -> tensor<?x1xf32>
  %2 = "tf.Sub"(%arg1, %0) : (tensor<4x1xf32>, tensor<f32>) -> tensor<4x1xf32>
  func.return %1, %2: tensor<?x1xf32>, tensor<4x1xf32>

// CHECK: return %arg0, %arg1
}

  return {};
}

// Convert Pack to Reshape when there is only one operand to be packed.
// For example,
//
//   %0 = tf.Pack(%input) {axis = 0} // %input : tensor<2x3xf32>
//
// can be canonicalized to
//
//   %shape = "tf.Const"() {value = dense<[1, 2, 3]> : tensor<3xi64>}
//   %0 = tf.Reshape(%input, %shape)
struct ConvertPackToReshape : public OpRewritePattern<PackOp> {