return %8 : tensor<*xf32>
  }

// CHECK-LABEL: conv_with_bias_and_relu
// CHECK-DAG: %[[cst:.*]] = arith.constant dense<[7.11401462, 7.05456924]> : tensor<2xf32>
// CHECK-DAG: %[[cst_1:.*]] = arith.constant dense<{{.*}}> : tensor<2x3x3x2xf32>

// CHECK: %[[q0:.*]] = "quantfork.qcast"(%[[cst]]) {volatile}
// CHECK-SAME: tensor<2x!quant.uniform<i32:f32, 0.044169864606680966>>

// CHECK-LABEL: @unfold_splat_constant_float
func.func @unfold_splat_constant_float() -> tensor<1x750xf32> {
  %cst = mhlo.constant dense<7.680000e+02> : tensor<1x750xf32>
  func.return %cst : tensor<1x750xf32>

  // CHECK-DAG: %0 = mhlo.constant dense<7.680000e+02> : tensor<f32>

module attributes {tf_saved_model.semantics} {
  func.func private @quantize_dot_general_fn(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> attributes {tf._original_func_name = "main_0"} {
    %cst = stablehlo.constant dense<3.000000e-01> : tensor<2x3xf32>
    %0 = "quantfork.qcast"(%cst) : (tensor<2x3xf32>) -> tensor<2x3x!quant.uniform<i8:f32, 6.000000e-03>>
    %1 = "quantfork.dcast"(%0) : (tensor<2x3x!quant.uniform<i8:f32, 6.000000e-03>>) -> tensor<2x3xf32>

// CHECK-LABEL: lift_float_matmul
func.func @lift_float_matmul(%arg0: tensor<1x12x12x512xf32>) -> (tensor<*xf32>, tensor<*xf32>) {
  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<512x512xf32>} : () -> tensor<512x512xf32>
  %out_1 = "tf.MatMul"(%arg0, %cst) {
    device = "", transpose_a = false, transpose_b = false
  } : (tensor<1x12x12x512xf32>, tensor<512x512xf32>) -> tensor<*xf32>
  %out_2 = "tf.MatMul"(%arg0, %arg0) {

// RUN: json_to_flatbuffer %p/test_schema.fbs %s | flatbuffer_translate --tflite-flatbuffer-to-mlir -o - | FileCheck %s

// CHECK: %[[CST:.*]] = "tfl.no_value"() <{value}> : () -> none
// CHECK: %[[RES0:.*]] = "tfl.conv_2d"(%arg0, %arg1, %[[CST]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, none) -> tensor<256x32x32x16xf32>

    %0 = tf_executor.graph {
      %outputs, %control = tf_executor.island {
        %cst = "tf.Const"() <{value = dense<false> : tensor<i1>}> : () -> tensor<*xi1>
        tf_executor.yield %cst : tensor<*xi1>
      }
      tf_executor.fetch %outputs : tensor<*xi1>
    }
    return %0 : tensor<*xi1>
  }

  %cst = arith.constant dense<0> : tensor<i32> loc("Const")
  %0 = "tfl.greater"(%arg0, %cst) : (tensor<*xi32>, tensor<i32>) -> tensor<i1>
  func.return %0 : tensor<i1>
}

func.func @body(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>) -> (tensor<*xi32>, tensor<*xf32>) {
  %cst = arith.constant dense<1> : tensor<i32> loc("Const")

  func.return %2 : tensor<256x8x7x16xf32>

// CHECK-DAG: %[[cst:.*]] = "tf.Const{{.*}} dense<8.000000e+00> : tensor<3x3x3x16xf32>
// CHECK-DAG: %[[cst_0:.*]] = "tf.Const{{.*}} dense<1.200000e+01> : tensor<16xf32>
// CHECK-NEXT: %[[conv:.*]] = "tf.Conv2D"(%arg0, %[[cst]])
// CHECK-NEXT: %[[bias:.*]] = "tf.AddV2"(%[[conv]], %[[cst_0]])
// CHECK-NEXT: return %[[bias]] : tensor<256x8x7x16xf32>
}

  return %1 : tensor<1x3xf32>
}
// CHECK: @main(%[[ARG_0:.+]]: tensor<1x4xf32>) -> tensor<1x3xf32>
// CHECK-DAG: %[[CST:.+]] = stablehlo.constant dense<1.000000e+00> : tensor<4x3xf32>

  func.return %3 : tensor<2x1xf32>
}

// CHECK: %[[CST:.*]] = arith.constant dense<1> : tensor<4xi32>
// CHECK:  [[VAL_0:%.*]] = "tfl.reshape"(%1, %[[CST]]) {tac.device = "GPU",  tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>
// CHECK:  [[VAL_1:%.*]] = "tfl.reshape"(%2, %[[CST]]) {tac.device = "GPU",  tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>