return op_ptr->Execute(absl::MakeSpan(backprops, 1), &num_retvals);
}

// Op: Relu()
// Summary: Computes rectified linear: `max(features, 0)`.
//
// Description:
//   See: https://en.wikipedia.org/wiki/Rectifier_(neural_networks)
//   Example usage:
//   >>> tf.nn.relu([-2., 0., 3.]).numpy()
//   array([0., 0., 3.], dtype=float32)
Status Relu(AbstractContext* ctx, AbstractTensorHandle* const features,

    value {
      s: "NHWC"
    }
  }
}
node {
  name: "conv_net_2d_1/Relu"
  op: "Relu"
  input: "conv_net_2d_1/conv_2d_0/BiasAdd"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "output_0"
  op: "Identity"
  input: "conv_net_2d_1/Relu"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}

  // CHECK: %[[TANH:[0-9]*]] = "tf.Tanh"(%[[ARG_TRANSPOSE]]) {{.*}} tensor<1x8x4x4xf32>
  // CHECK: %[[RELU:[0-9]*]] = "tf.Relu"(%[[TANH]]) {{.*}} tensor<1x8x4x4xf32>
  // CHECK: return %[[RELU]]

  %0 = "tf.Tanh"(%arg0) : (tensor<1x4x4x8xf32>) -> tensor<1x4x4x8xf32>
  %1 = "tf.Relu"(%0) : (tensor<1x4x4x8xf32>) -> tensor<1x4x4x8xf32>

  %2 = "tf.Const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>

  } : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>
  %4 = "tf.BiasAdd"(%3, %cst) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32>
  %5 = "tf.Relu"(%4) {device = ""} : (tensor<*xf32>) -> tensor<*xf32>


  %6 = "tf.Conv2D"(%arg0, %arg1) {
    data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [],

// CHECK-SAME: f = @composite_conv3d_fn_1}>
// CHECK-NOT: {_tfl_quant_trait = "fully_quantizable"
// CHECK: %[[RELU:.*]] = "tf.Relu"(%[[PARTITIONEDCALL_0]])
// CHECK: return %[[RELU]]

// CHECK-LABEL: private @composite_conv3d_fn_1

// WEIGHTONLY-DAG: %[[CST:.*]] = "tf.Const"() {{.*}} : () -> tensor<2x3x3x3x2xf32>
// WEIGHTONLY: %[[PARTITIONEDCALL_0:.*]] = "tf.PartitionedCall"(%arg0, %[[CST]])

    Node* add1 =
        ops::BinaryOp("Add", unstage, b, builder.opts().WithName("add1"));
    Node* relu0 = ops::UnaryOp("Relu", add0, builder.opts().WithName("relu0"));
    ops::UnaryOp("Relu", add1, builder.opts().WithName("relu1"));
    BuildStageNode(builder, "stage", {DT_FLOAT}, {relu0});

    TF_EXPECT_OK(GraphDefBuilderToGraph(builder, graph.get()));
  }

// Computes rectified linear gradients for a Relu operation.
Status ReluGrad(AbstractContext* ctx, AbstractTensorHandle* const gradients,
                AbstractTensorHandle* const features,
                AbstractTensorHandle** backprops, const char* name = nullptr,
                const char* raw_device_name = nullptr);

// Computes rectified linear: `max(features, 0)`.
Status Relu(AbstractContext* ctx, AbstractTensorHandle* const features,

using tensorflow::TF_StatusPtr;

Status ReluModel(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> inputs,
                 absl::Span<AbstractTensorHandle*> outputs) {
  return ops::Relu(ctx, inputs[0], &outputs[0], "Relu");
}

Status SparseSoftmaxCrossEntropyWithLogitsModel(
    AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs,
    absl::Span<AbstractTensorHandle*> outputs) {

        tensor_content: "\001"
      }
    }
  }
  experimental_debug_info {
    original_node_names: "bn2a_branch1/cond_1"
  }
}
node {
  name: "activation_50/Relu"
  op: "Relu"
  input: "bn_conv1_1/FusedBatchNormV2"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "T"
    value {
      type: DT_HALF
    }
  }
}

    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "data_format"
    value {
      s: "NHWC"
    }
  }
}
node {
  name: "sequential/quant_dense/Relu"
  op: "Relu"
  input: "sequential/quant_dense/BiasAdd"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
node {