-> (tensor<4x256xf16>) {
  %0 = "mhlo.batch_norm_inference"(%x, %scale, %offset, %mean, %variance)
      {epsilon = 1.0 : f32, feature_index = 1 : i64} :
      (tensor<4x256xf16>, tensor<256xf16>, tensor<256xf16>, tensor<256xf16>,
        tensor<256xf16>) -> tensor<4x256xf16>
  func.return %0 : tensor<4x256xf16>
}

// Validate that epsilon is overflow

       {
         data_format = "NCHW",
         epsilon = 1.001000e-05 : f32
       } : (tensor<?x256x56x56xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>)
        -> (tensor<?x256x56x56xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<*xf32>)

  // CHECK: %[[BATCH_NORM1:[_a-z0-9]*]], {{.*}} = "tf.FusedBatchNormV3"
  // CHECK-SAME: %[[CONV1]]

func.func @unfuse_batch_norm(
    %x: tensor<4x256xf32>, %scale: tensor<256xf32>, %offset: tensor<256xf32>,
    %mean: tensor<256xf32>, %variance: tensor<256xf32>)
    -> (tensor<4x256xf32>) {
  // CHECK-DAG: %[[EPS_BCAST:.+]] = mhlo.constant dense<1.001000e-05> : tensor<256xf32>
  // CHECK-DAG: %[[VARIANCE_EPS:.+]] = mhlo.add %[[VARIANCE]], %[[EPS_BCAST]] : tensor<256xf32>
  // CHECK-DAG: %[[STDDEV:.+]] = mhlo.sqrt %[[VARIANCE_EPS]] : tensor<256xf32>

  func.return %y : tensor<1x64x28x28xf32>
}

// CHECK-LABEL: bias_add_nchw
func.func @bias_add_nchw(%arg0: tensor<1x256x150x150xf32>, %arg1: tensor<256xf32>) -> tensor<1x256x150x150xf32> {
  // CHECK: (%[[ARG0:.*]]: tensor<1x256x150x150xf32>, %[[ARG1:.*]]: tensor<256xf32>)
  // CHECK: %[[CST:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi64>}>
  // CHECK: %[[R0:.*]] = "tf.Transpose"(%[[ARG0]], %[[CST]])

// CHECK:           return %[[VAL_4]] : tensor<2x3xf16>
// CHECK:         }
func.func @floordiv_f16_broadcast(%arg0: tensor<2x3xf16>, %arg1: tensor<3xf16>) -> tensor<2x3xf16> {
  %0 = "chlo.broadcast_divide"(%arg0, %arg1) {broadcast_dimensions = array<i64: 1>} : (tensor<2x3xf16>, tensor<3xf16>) -> tensor<2x3xf16>

  %cst_0 = "tf.Const"() {value = dense<0.400000e+00> : tensor<256xf32>} : () -> tensor<256xf32>
  %cst_1 = "tf.Const"() {value = dense<0.500000e+00> : tensor<256xf32>} : () -> tensor<256xf32>
  %w = "tf.AddV2"(%cst, %cst_1) : (tensor<48x48x3x1xf32>, tensor<256xf32>) -> tensor<48x48x3x256xf32>

  %0 = "tfl.conv_2d"(%arg0, %arg1, %cst_0) {dilation_h_factor = 2 : i32, dilation_w_factor = 3 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 4 : i32, stride_w = 5 : i32} : (tensor<256x32x32x3xf16>, tensor<16x3x3x3xf16>, tensor<16xf16>) -> tensor<256x8x7x16xf16>

:meta-name-twitter_title: Gradle User Manual: Version {gradleVersion}
:meta-name-twitter_description: {description}
:meta-name-twitter_url: {docsUrl}/{gradleVersion}/userguide/{docname}.html
:meta-name-twitter_image: {website}/images/gradle-256x256.png

toc::[leveloffset=+2]

[[part:about_gradle]]
== **OVERVIEW**

include::userguide.adoc[leveloffset=+2]
include::about_manual.adoc[leveloffset=+2]

'''

[[part:releases]]
== **RELEASES**

}

// CHECK-LABEL: func @testEmptyf16
func.func @testEmptyf16() -> (tensor<5xf16>) {
  %0 = "tf.Const"() { value = dense<5> : tensor<i32> } : () -> tensor<i32>

  // CHECK: [[VAL:%.+]] = "tf.Const"() <{value =  dense<0.000000e+00> : tensor<5xf16>}>
  // CHECK: return [[VAL]]
  %1 = "tf.Empty"(%0) : (tensor<i32>) -> (tensor<5xf16>)
  func.return %1 : tensor<5xf16>
}

// CHECK-LABEL: func @testEmptybf16

// Float16-DAG: %[[b:.*]] = arith.constant dense<0.000000e+00> : tensor<16xf16>
// Float16-DAG: %[[const:.*]] = "tfl.no_value"() <{value}> : () -> none
// Float16-DAG: %[[dq_w:.*]] = "tfl.dequantize"(%[[w]]) : (tensor<3x3x3x8x16xf16>) -> tensor<3x3x3x8x16xf32>
// Float16-DAG: %[[dq_b:.*]] = "tfl.dequantize"(%[[b]]) : (tensor<16xf16>) -> tensor<16xf32>