func.return %1 : tensor<5x2x3x4xf32>
}

// CHECK: %cst = arith.constant dense<1.000000e+00> : tensor<5x2x3x4xf32>
// CHECK: %0 = "tfl.transpose"(%arg0, %arg1) : (tensor<2x3x4x5xf32>, tensor<4xi32>) -> tensor<5x2x3x4xf32>
// CHECK: %1 = tfl.add %0, %cst {fused_activation_function = "NONE"} : tensor<5x2x3x4xf32>
// CHECK: return %1 : tensor<5x2x3x4xf32>

// -----

// CHECK-LABEL: doubleTposeInput

// CHECK-LABEL: transpose_simple_4d
func.func @transpose_simple_4d() -> tensor<5x2x3x4xf32> {
  %0 = stablehlo.constant dense<1.000000e+0> : tensor<2x3x4x5xf32>
  %1 = stablehlo.transpose %0, dims = [3, 0, 1, 2] : (tensor<2x3x4x5xf32>) -> tensor<5x2x3x4xf32>
  return %1 : tensor<5x2x3x4xf32>
}
// CHECK-DAG: %[[CONST_0:.+]] = stablehlo.constant dense<1.000000e+00> : tensor<5x2x3x4xf32>
// CHECK-NOT: transpose
// CHECK: return %[[CONST_0]] : tensor<5x2x3x4xf32>

  %0 = stablehlo.constant() {value = dense<7.000000e+00> : tensor<8x3x3x8xf32>} : () -> tensor<8x3x3x8xf32>
  %2 = stablehlo.convolution(%arg0, %0) dim_numbers = [b, f, 0, 1]x[i, 0, 1, o]->[b, f, 0, 1], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x8x4x4xf32>, tensor<8x3x3x8xf32>) -> tensor<1x8x4x4xf32>
  return %2 : tensor<1x8x4x4xf32>
}