%cst_add = arith.constant dense<2.0> : tensor<2x512xf32>  // Not 1D
  %cst_weights = arith.constant dense<3.0> : tensor<1024x512xf32>
  %cst_bias = arith.constant dense<5.0> : tensor<1024xf32>

  %0 = "tfl.add"(%arg, %cst_add) {fused_activation_function = "NONE"} : (tensor<2x512xf32>, tensor<2x512xf32>) -> tensor<2x512xf32>

// CHECK:           }) : (tensor<5x4x3x7xf32>, tensor<2x2xi32>, tensor<2x5x3xf32>) -> tensor<5x4x3x7xf32>
// CHECK:           return %[[VAL_3]] : tensor<5x4x3x7xf32>
// CHECK:         }
func.func @convert_scatter_update_to_non_trailing_operand_dimensions(
  %arg0: tensor<5x4x3x7xf32>,
  %arg1: tensor<2x2xi32>,
  %arg2: tensor<2x5x3xf32>) -> tensor<5x4x3x7xf32>
{
  %0 = "mhlo.scatter"(%arg0, %arg1, %arg2) ({

func.func @conv2d_backprop_input_grouped(
    %filter: tensor<2x2x5x21xf32>,
    %out_backprop: tensor<5x2x2x21xf32>
  ) -> tensor<5x3x3x15xf32> {
  %input_sizes = "tf.Const" () { value = dense<[5, 3, 3, 15]> : tensor<4xi32> } : () -> tensor<4xi32>

  // Verify filter transformation for grouped convolution.

  // CHECK: %[[RESHAPE:.*]] = mhlo.reshape %arg0 : (tensor<2x2x5x21xf32>) -> tensor<2x2x5x3x7xf32>