return result;
}

// The backprop loop counter and main backprop loop run in their own execution
// frame (conceptually, the main forward loop and forward loop counter run
// together in a frame, then the backprop loop counter and backprop loop run
// together in a different frame). This returns the frame name to use for the
// backprop while loops.

  *loss = temp_outputs[0];
  *backprop = temp_outputs[1];
  return status;
}

// Op: ReluGrad()
// Summary: Computes rectified linear gradients for a Relu operation.
//
// Description:
Status ReluGrad(AbstractContext* ctx, AbstractTensorHandle* const gradients,
                AbstractTensorHandle* const features,
                AbstractTensorHandle** backprops, const char* name,

    # compute with op.
    with backprop.GradientTape() as gt:
      for var_ in vars_:
        gt.watch(var_)
      y = compute_op(**op_kwargs)  # uses op and decomposites by the graph pass.
      grads = gt.gradient(y, vars_)  # uses registered gradient function.

    # compute with composition
    with backprop.GradientTape() as gt:
      for var_ in vars_:
        gt.watch(var_)

    absl::Span<AbstractTensorHandle*> outputs) {
  AbstractTensorHandle* loss;
  AbstractTensorHandle* backprop;
  TF_RETURN_IF_ERROR(ops::SparseSoftmaxCrossEntropyWithLogits(
      ctx, inputs[0], inputs[1], &loss, &backprop,
      "SparseSoftmaxCrossEntropyWithLogits"));
  // `gradient_checker` only works with model that returns only 1 tensor.

//
Status SparseSoftmaxCrossEntropyWithLogits(AbstractContext* ctx, AbstractTensorHandle* const features, AbstractTensorHandle* const labels, AbstractTensorHandle** loss, AbstractTensorHandle** backprop, const char* name = nullptr, const char* raw_device_name = nullptr);

//

    AbstractTensorHandle** backprop, const char* name = nullptr,
    const char* raw_device_name = nullptr);

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

// Summary:
//
// Description:
Status SparseSoftmaxCrossEntropyWithLogits(AbstractContext* ctx, AbstractTensorHandle* const features, AbstractTensorHandle* const labels, AbstractTensorHandle** loss, AbstractTensorHandle** backprop, const char* name, const char* raw_device_name) {
  AbstractOperationPtr op_ptr(ctx->CreateOperation());
  TF_RETURN_IF_ERROR(op_ptr->Reset("SparseSoftmaxCrossEntropyWithLogits", raw_device_name));

//   backprop such that dx = g(dy). In Python,
//
//   ```python
//   with tf.get_default_graph().gradient_override_map(
//       {'IdentityN': 'OverrideGradientWithG'}):
//     y, _ = identity_n([f(x), x])
//
//   @tf.RegisterGradient('OverrideGradientWithG')
//   def ApplyG(op, dy, _):
//     return [None, g(dy)]  # Do not backprop to f(x).
//   ```
Status IdentityN(AbstractContext* ctx,

      });
    }
    filter_value = filter.getValue();
    mul_value = multiplier.getValue();
    // In MHLO, Conv filter is in HWIO format, Depthwise conv filter is in HW1O
    // format and backprop input conv filter is in HWOI format.
    // Only fuses multiplier if all dimensions other than the out channel
    // dimension are equal to 1.
    if (!TFL::IsDimensionsDegenerateExceptLastOne(

  %arg2: tensor<64xf32>
) -> tensor<1x28x28x64xf32> {

  // CHECK: "tf.FusedBatchNormGradV3"
  // CHECK-SAME: (%[[X_TRANSPOSE:[0-9]*]], %[[Y_TRANSPOSE:[0-9]*]],
  // CHECK-SAME: data_format = "NCHW"
  %x_backprop, %scale_backprop, %offset_backprop, %reserve_1, %reserve_2
    = "tf.FusedBatchNormGradV3"(%arg0, %arg1, %arg2, %arg2, %arg2, %arg2)
       {
         data_format = "NHWC",
         epsilon = 1.001 : f32,