func.return %1 : tensor<3x2xi32>
}

// CHECK: %cst = arith.constant dense<[1, 0]> : tensor<2xi32>
// CHECK: [1, 3, 5], [2, 4, 6]
// CHECK: %0 = tfl.add %arg0, %cst_0 {fused_activation_function = "NONE"} : tensor<2x3xi32>
// CHECK: %1 = "tfl.transpose"(%0, %cst) : (tensor<2x3xi32>, tensor<2xi32>) -> tensor<3x2xi32>
// CHECK: return %1 : tensor<3x2xi32>

// -----

// CHECK-LABEL: pushTposeAfterSubSimple

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

// CHECK: %0 = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32>
// CHECK: %1 = "tfl.reshape"(%arg0, %0) : (tensor<1x4xf32>, tensor<2xi32>) -> tensor<2x2xf32>

{
  "version": 3,
  "operator_codes": [
    {
      "builtin_code": "RESHAPE"
    }
  ],
  "subgraphs": [
    {
      "tensors": [

  func.return %2 : tensor<1x6x6x16xf32>

// CHECK: %0 = "tfl.dequantize"(%arg0)
// CHECK: %1 = "tfl.dequantize"(%arg1)
// CHECK: %2 = "tfl.maximum"(%0, %1)
// CHECK: %3 = "tfl.quantize"(%2)
// CHECK: %4 = "tfl.dequantize"(%3)
// CHECK: return %4 : tensor<1x6x6x16xf32>
}

// CHECK-LABEL: QuantizeMinimum

// CHECK-LABEL: avg_pool2d_3
// CHECK: %cst = arith.constant dense<[0, 2, 3, 1]> : tensor<4xi32>
// CHECK: %0 = "tfl.transpose"(%arg0, %cst) : (tensor<1x1x1x8xf32>, tensor<4xi32>) -> tensor<1x1x8x1xf32>
// CHECK{LITERAL}: %cst_0 = arith.constant dense<[[0, 0], [0, 0], [1, 1], [0, 0]]> : tensor<4x2xi32>

  func.return %1 : tensor<2x1xf32>

// CHECK:  %cst = arith.constant
// CHECK:  %0 = "tf.Reshape"(%arg0, %cst)
// CHECK-SAME: tensor<2x1xf32>
// CHECK:  %1 = "tf.FakeQuantWithMinMaxVars"(%0, %arg1, %arg2)
// CHECK:  return %1
}

// CHECK-LABEL: fakeQuantFollowedByReshapes

  func.return %1 : tensor<2x1xf32>

// CHECK:  %cst = arith.constant
// CHECK:  %0 = "tf.Reshape"(%arg0, %cst)
// CHECK-SAME: tensor<2x1xf32>
// CHECK:  %1 = "tf.FakeQuantWithMinMaxVars"(%0, %arg1, %arg2)
// CHECK:  return %1
}

// CHECK-LABEL: fakeQuantFollowedByReshapes

// CHECK-LABEL: depthwiseConv2D
// CHECK-DAG:  %[[CONSTANT:.*]] = arith.constant dense<0.000000e+00> : tensor<12xf32>
// CHECK-DAG:  %[[CONSTANT0:.*]] = arith.constant dense<[1, 3, 3, 12]> : tensor<4xi32>
// CHECK:  %0 = "tf.Reshape"(%arg1, %[[CONSTANT0]]) : (tensor<3x3x3x4xf32>, tensor<4xi32>) -> tensor<1x3x3x12xf32>

  func.return %4 : i32
// CHECK-LABEL: squeezeAndReshape
// CHECK:  "tfl.squeeze"(%arg0) <{squeeze_dims = [0]}> : (tensor<1x1x10xf32>) -> tensor<1x10xf32>
// CHECK:  %1 = "tfl.squeeze"(%arg1) <{squeeze_dims = []}> : (tensor<?x10xf32>) -> tensor<*xf32>
// CHECK:  %cst = arith.constant dense<[2, 5]> : tensor<2xi32>
// CHECK:  %2 = "tfl.reshape"(%0, %cst) : (tensor<1x10xf32>, tensor<2xi32>) -> tensor<2x5xf32>

// CHECK:       %0 = "tfl.while"(%arg0) ({
// CHECK:       ^bb0(%arg1: tensor<i32>):
// CHECK:         %1 = tfl.add %arg0, %arg0 {fused_activation_function = "NONE", tac.device = "CPU", tac.inference_type = "FLOAT"} : tensor<i32>
// CHECK:         %2 = func.call @func_1_GPU_FLOAT(%1) {tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_1"} : (tensor<i32>) -> tensor<i32>

  return %2 : tensor<1x1024xf32>
  // CHECK: %cst = arith.constant dense<[1, 1024]> : tensor<2xi32>
  // CHECK: %cst_0 = arith.constant dense<[1, 1, 64]> : tensor<3xi32>
  // CHECK: %0 = "tfl.reshape"(%arg0, %cst_0) : (tensor<1x64xf32>, tensor<3xi32>) -> tensor<1x1x64xf32>