%0 = stablehlo.constant dense<4.000000e+03> : tensor<1024x3xf32>
    %1 = stablehlo.constant dense<1.000000e+03> : tensor<1024x3xf32>
    %2 = stablehlo.constant dense<5.000000e+01> : tensor<1024x3xf32>
    %3 = stablehlo.remainder %0, %1 : tensor<1024x3xf32>
    %4 = stablehlo.compare  EQ, %3, %2,  NOTYPE : (tensor<1024x3xf32>, tensor<1024x3xf32>) -> tensor<1024x3xi1>

func.func @concatConstantTensorsMiddleDim() -> tensor<1x4x3xi32> {
  %cst_0 = arith.constant dense<0> : tensor<1x2x3xi32>
  %cst_1 = arith.constant dense<1> : tensor<1x2x3xi32>
  %0 = "tfl.concatenation"(%cst_0, %cst_1) {axis = 1 : i32, fused_activation_function = "NONE"} : (tensor<1x2x3xi32>, tensor<1x2x3xi32>) -> tensor<1x4x3xi32>
  func.return %0 : tensor<1x4x3xi32>

tensor<32x1024xf32>, tensor<1024x1xf32>, tensor<1024xf32>, tensor<1024x1xf32>, tensor<i64>, tensor<i32>, tensor<32x1024xf32>, tensor<i64>, tensor<i64>, tensor<1024xf32>, tensor<32x1024xf32>, tensor<1024x1xf32>, tensor<1024xf32>, tensor<1024x1xf32>, tensor<i64>, tensor<i32>, tensor<32x1024xf32>, tensor<i64>, tensor<i64>, tensor<1024xf32>, tensor<32x1024xf32>, tensor<1024x1xf32>, tensor<1024xf32>, tensor<1024x1xf32>, tensor<i64>, tensor<i32>, tensor<32x1024xf32>, tensor<i64>, tensor<i64>, tensor<1024xf32>, tensor<32x1024xf32>,...

constexpr absl::string_view kModuleStatic = R"mlir(
  module {
    func.func @main(%arg0: tensor<1x1024xf32>, %arg1: tensor<1024x3xf32>) -> tensor<1x3xf32> attributes {_from_xla_call_module} {
      %0 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0], precision = [] : (tensor<1x1024xf32>, tensor<1024x3xf32>) -> tensor<1x3xf32>
      return %0 : tensor<1x3xf32>
    }
  }
)mlir";

  // Use a four dimension sharding (devices=[1,1,1,1]0)
  // Since the input tensor only has three dimensions, we expect this to fail.
  %0 = "tf.XlaSharding"(%arg0) { _XlaSharding = "\08\03\1A\04\01\01\01\01\22\01\00" } : (tensor<1x2x3xi32>) -> tensor<1x2x3xi32>
  %1 = "tf.A"(%0) : (tensor<1x2x3xi32>) -> (tensor<1x2x3xi32>)
  func.return %1: tensor<1x2x3xi32>
}

// -----

// CHECK-LABEL: func @check_retval_sharding_errors

    %cst_0 = "tf.Const"() {value = dense<2.000000e+00> : tensor<1024x2xf32>} : () -> tensor<1024x2xf32>
    %cst_1 = "tf.Const"() {value = dense<[2, 1024]> : tensor<2xi32>} : () -> tensor<2xi32>
    // Original identity preserved.
    %cst_identity = "tf.Identity"(%cst_0) {device = ""} : (tensor<1024x2xf32>) -> tensor<1024x2xf32>
    %0 = "tf.Reshape"(%cst_identity, %cst_1) : (tensor<1024x2xf32>, tensor<2xi32>) -> tensor<2x1024xf32>

  module {
    func.func private @composite_dot_general_fn_1(%arg0: tensor<1x1024xf32>, %arg1: tensor<1024x3xf32>) -> tensor<1x3xf32> attributes {_from_xla_call_module, tf_quant.composite_function} {
      %0 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0], precision = [DEFAULT, DEFAULT] : (tensor<1x1024xf32>, tensor<1024x3xf32>) -> tensor<1x3xf32>
      return %0 : tensor<1x3xf32>
    }
  }
)mlir";