func.func @bias_add(%arg0: tensor<1x10x10x32xf32>, %arg1: tensor<32xf32>) -> tensor<1x10x10x32xf32> {
  %0 = "tf.BiasAdd"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32>
  func.return %0 : tensor<1x10x10x32xf32>
// CHECK: %[[BIASADD_0:.*]] = "tf.BiasAdd"(%arg0, %arg1) <{data_format = "NHWC"}> {T = "tfdtype$DT_FLOAT"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32>

# MLIR-SAME:  control_outputs = ""
# MLIR-SAME:  inputs = "input"
# MLIR-SAME:  outputs = "output"
# MLIR:         %[[shape:.*]] = arith.constant dense<[1, -1, 31]> : tensor<3xi32>
# MLIR:         %[[bias:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x!quant.uniform<i32:f32:0
# MLIR:         %[[weight:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x1x1x256x!quant.uniform<i4<-7:7>:f32:0, {2.2825599397931779,

// CHECK-NEXT:  buffer: 10
// CHECK-NEXT:  } ]
// CHECK-NEXT:  signature_defs: [ ]
// CHECK-NEXT:}

  %0 = "tfl.pseudo_const" () {value = dense<[1, 401408]> : tensor<2xi32>} : () -> tensor<2xi32> loc("Const")
  %1 = "tfl.quantize"(%arg0) {qtype = tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>} : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>

    -> (tensor<?x2xi64>, tensor<?x2xi64>, tensor<?x!tf_type.string>, tensor<?xi64>, tensor<2xi64>, tensor<2xi64>, tensor<32xf32>, tensor<32xf32>)

  // CHECK: tfrt.return {{.*}}, [[outputs]]#0
  func.return %outputs#0 : tensor<?x2xi64>
}

// CHECK-LABEL: func @no_native

# MLIR-SAME:  control_outputs = ""
# MLIR-SAME:  inputs = "input"
# MLIR-SAME:  outputs = "output"
# MLIR:         %[[shape:.*]] = arith.constant dense<[1, -1, 31]> : tensor<3xi32>
# MLIR:         %[[bias:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x!quant.uniform<i32:f32:0

  func.return %1 : tensor<1x1x257xi32>
  // CHECK: %[[CONST_DIM:.*]] = "tfl.pseudo_const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>
  // CHECK: %[[RED:.*]] = "tfl.sum"(%arg0, %[[CONST_DIM]]) <{keep_dims = true}> : (tensor<1x16384x257xi32>, tensor<1xi32>) -> tensor<1x1x257xi32>
}

// CHECK-LABEL: BatchmatmulToReduceSumF32
// CHECK-NOT: "tfl.batch_matmul"

  %b = arith.constant dense<0.0> : tensor<3xf32>
  %b2 = arith.constant dense<[1.0e-2, 2.1473647e1, -2.1473647e2]> : tensor<3xf32>
  %conv = "tfl.conv_2d"(%0, %w, %b) {
    dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU",
    padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32
  } : (tensor<1x5x5x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<1x5x5x3xf32>

000000b0  20 4c 74 64 30 1e 17 0d  31 32 31 31 32 32 31 35  | Ltd0...12112215|
000000c0  30 36 33 32 5a 17 0d 32  32 31 31 32 30 31 35 30  |0632Z..221120150|
000000d0  36 33 32 5a 30 45 31 0b  30 09 06 03 55 04 06 13  |632Z0E1.0...U...|
000000e0  02 41 55 31 13 30 11 06  03 55 04 08 13 0a 53 6f  |.AU1.0...U....So|
000000f0  6d 65 2d 53 74 61 74 65  31 21 30 1f 06 03 55 04  |me-State1!0...U.|

000000b0  20 4c 74 64 30 1e 17 0d  31 32 31 31 32 32 31 35  | Ltd0...12112215|
000000c0  30 36 33 32 5a 17 0d 32  32 31 31 32 30 31 35 30  |0632Z..221120150|
000000d0  36 33 32 5a 30 45 31 0b  30 09 06 03 55 04 06 13  |632Z0E1.0...U...|
000000e0  02 41 55 31 13 30 11 06  03 55 04 08 13 0a 53 6f  |.AU1.0...U....So|
000000f0  6d 65 2d 53 74 61 74 65  31 21 30 1f 06 03 55 04  |me-State1!0...U.|

  let description = [{
    ConstDenseTensorOp creates a constant dense tensorflow tensor.

    Example:
      %tensor = tfrt_fallback_async.const_dense_tensor dense<[0, 1, 2]> : tensor<3xi32>
  }];

  let arguments = (ins
    ElementsAttr:$value
  );

  let results = (outs
    TFTensorType:$result
  );

  let assemblyFormat = "$value attr-dict";

  let hasFolder = 1;