All rights reserved.

All other contributions:

Copyright (c) 2014, the respective contributors
All rights reserved.

Caffe uses a shared copyright model: each contributor holds copyright over
their contributions to Caffe. The project versioning records all such
contribution and copyright details. If a contributor wants to further mark

namespace gradients {
namespace internal {

void CompareNumericalAndAutodiffGradients(
    Model model, Model grad_model, AbstractContext* ctx,
    absl::Span<AbstractTensorHandle* const> inputs, bool use_function,
    double abs_error) {
  auto num_inputs = inputs.size();
  std::vector<AbstractTensorHandle*> outputs(num_inputs);
  auto s = RunModel(grad_model, ctx, inputs, absl::MakeSpan(outputs),
                    /*use_function=*/use_function);

  cat > $BATS_TEST_TMPDIR/ignore_these_deps <<EOF
benchmark
_test$
_test.py$
_test_cpu$
_test_cpu.py$
_test_gpu$
_test_gpu.py$
_test_lib$
//tensorflow/cc/saved_model:saved_model_test_files
//tensorflow/cc/saved_model:saved_model_half_plus_two
//tensorflow:no_tensorflow_py_deps
//tensorflow/tools/pip_package:win_pip_package_marker
//tensorflow/core:image_testdata
//tensorflow/core/kernels/cloud:bigquery_reader_ops

    Model MatMulModel =
        [transpose_a, transpose_b](
            AbstractContext* ctx,
            absl::Span<AbstractTensorHandle* const> inputs,
            absl::Span<AbstractTensorHandle*> outputs) -> Status {
      return ops::MatMul(ctx, inputs[0], inputs[1], &outputs[0], transpose_a,
                         transpose_b, "MatMul");
    };

authors:
  - family-names: Abadi
    given-names: Martín
  - family-names: Agarwal
    given-names: Ashish

# BEGIN TF CACHE HELPER OPTIONS
# Options when using remote execution

      RunModel(forward, ctx, inputs, model_outputs, use_function));
  AbstractTensorHandlePtr model_out(model_outputs[0]);

  TF_Tensor* model_out_tensor;
  TF_RETURN_IF_ERROR(GetValue(model_out.get(), &model_out_tensor));
  int num_dims_out = TF_NumDims(model_out_tensor);
  TF_DeleteTensor(model_out_tensor);

  // If the output is a scalar, then return the scalar output
  if (num_dims_out == 0) {
    outputs[0] = model_out.release();

Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to Colab/Jupyter/any notebook.

Also, please include a link to a GraphDef or the model if possible.

**Any other info / logs**

Include any logs or source code that would be helpful to diagnose the problem.
If including tracebacks, please include the full traceback. Large logs and files

      description: If compiling from source
  - type: input
    id: Cuda
    attributes:
      label: CUDA/cuDNN version
  - type: input
    id: Gpu
    attributes:
      label: GPU model and memory
      description: If compiling from source
  - type: textarea
    id: what-happened
    attributes:
      label: Current behavior?
      description: Also tell us, what did you expect to happen?

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