%0 = "tfl.dequantize"(%arg0) : (tensor<1x6x6x16x!quant.uniform<u8:f32, 0.1>>) -> tensor<1x6x6x16xf32>
  %1 = "tfl.dequantize"(%arg1) : (tensor<1x6x6x16x!quant.uniform<u8:f32, 0.1>>) -> tensor<1x6x6x16xf32>
  %2 = "tfl.minimum"(%0, %1) : (tensor<1x6x6x16xf32>, tensor<1x6x6x16xf32>) -> tensor<1x6x6x16xf32>
  func.return %2 : tensor<1x6x6x16xf32>

// CHECK: %0 = "tfl.dequantize"(%arg0)
// CHECK: %1 = "tfl.dequantize"(%arg1)

  // CHECK-NOT: tf.Lgamma
  %0 = "tf.Lgamma"(%arg0) : (tensor<4xf32>) -> tensor<4xf32>
  func.return %0 : tensor<4xf32>
}

// CHECK-LABEL: func @imag_resize_nearest
func.func @imag_resize_nearest(%arg0: tensor<1x7x7x1xi32>) -> tensor<1x3x3x1xi32> {
  %shape = "tf.Const"() {device = "", value = dense<3> : tensor<2xi32>} : () -> tensor<2xi32>

  // CHECK-DAG: [[VAL0:%.+]] = "tf.Const"() <{value = dense<1> : tensor<i32>}>

  // Move binary op batched RHS before reshape:
  // binary(reshape(lhs), rhs) => reshape(binary(lhs, flatten(rhs)))
  // Pattern targetted here is as follows-
  // [input, lhr, rhs] == [<1x1024x128>, <1x1024x8x16>, <1x1x8x16xf32>]
  // This is valid only when the-
  // 1.last dimension of lhs is equal to the number of elements in constant rhs.
  // 2.Reduded shape of rhs, here <8x16> is equal to last dimensions of lhs.