// CHECK-LABEL: @NotReorderReshapeAddIfNotTailingDimAfter
func.func @NotReorderReshapeAddIfNotTailingDimAfter(%arg0: tensor<1x30x1x96xf32>) -> tensor<1x30x96xf32> {
  %cst = arith.constant dense<2.0> : tensor<1x30x96xf32>
  %shape = arith.constant dense<[1, 30, 96]> : tensor<3xi32>
  %1 = "tfl.reshape"(%arg0, %shape) : (tensor<1x30x1x96xf32>, tensor<3xi32>) -> tensor<1x30x96xf32>

// CHECK:           %[[VAL_2:.*]] = "tf.Reshape"(%[[VAL_0]], %[[VAL_1]]) : (tensor<3x1x16xf32>, tensor<4xi64>) -> tensor<3x1x1x16xf32>
// CHECK:           %[[VAL_3:.*]] = arith.constant dense<[3, 8, 8, 16]> : tensor<4xi64>
// CHECK:           %[[VAL_4:.*]] = "tf.BroadcastTo"(%[[VAL_2]], %[[VAL_3]]) : (tensor<3x1x1x16xf32>, tensor<4xi64>) -> tensor<3x8x8x16xf32>
// CHECK:           return %[[VAL_4]] : tensor<3x8x8x16xf32>
// CHECK:         }

}

// -----

// CHECK-LABEL: @conv2d_backprop_filter_grouped
func.func @conv2d_backprop_filter_grouped(
    %input: tensor<1x2x2x2xf32>,
    %out_backprop: tensor<1x1x1x2xf32>
  ) -> tensor<2x2x1x2xf32> {

  // CHECK: mhlo.convolution(%arg0, %arg1)
  // CHECK-SAME:  batch_group_count = 2 : i64
  // CHECK-SAME:  feature_group_count = 1 : i64