namespace mlir {
namespace TF {

// Verifies an reduction op's `input` and reduction `dims`.
LogicalResult VerifyReductionInputAndDims(Value input, Value dims,
                                          Location loc) {
  auto dims_type = mlir::dyn_cast<RankedTensorType>(dims.getType());
  if (!dims_type) return success();
  if (dims_type.getRank() > 1)

    absl::string_view node_shapes_str) {
  std::vector<int> dims;
  for (absl::string_view dim_str : absl::StrSplit(node_shapes_str, ',')) {
    // Treats empty input shape as scalar
    if (dim_str.empty()) continue;
    if (dim_str == "?") {
      dims.push_back(-1);
      continue;
    }
    int size;
    TF_RET_CHECK(absl::SimpleAtoi(dim_str, &size));
    dims.push_back(size);
  }
  return dims;
}

# See the OWNERS docs at https://go.k8s.io/owners

reviewers:
  - bentheelder
  - dims
approvers:
  - dims
labels:
  - sig/testing

// TODO(jpienaar): Move these functions to some debug utils.
static std::string TfLiteTensorDimString(const TfLiteTensor& tensor) {
  auto begin = tensor.dims ? tensor.dims->data : nullptr;
  auto end = tensor.dims ? tensor.dims->data + tensor.dims->size : nullptr;
  return absl::StrJoin(begin, end, ", ");
}

template <typename T>
static std::string TfLiteTypedTensorString(const TfLiteTensor& tensor) {

}

TF_Tensor* Int8Tensor(const int64_t* dims, int num_dims, const char* values) {
  int64_t num_values = 1;
  for (int i = 0; i < num_dims; ++i) {
    num_values *= dims[i];
  }
  TF_Tensor* t =
      TF_AllocateTensor(TF_INT8, dims, num_dims, sizeof(char) * num_values);
  memcpy(TF_TensorData(t), values, sizeof(char) * num_values);
  return t;
}

TF_Tensor* Int32Tensor(const int64_t* dims, int num_dims,

  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {
    for (int d = 0; d < num_dims; ++d) {
      proto.add_dim()->set_size(dims[d]);
    }
  }

  forward_op_.attrs.Set(attr_name, proto);
  return parent_op_->SetAttrShape(attr_name, dims, num_dims);
}
Status TapeOperation::SetAttrFunction(const char* attr_name,
                                      const AbstractOperation* value) {

    int num_dims = output_shapes->items[0].num_dims;
    int64_t* dims = output_shapes->items[0].dims;

    if (!expected_shape.has_value()) {
      EXPECT_EQ(num_dims, -1);
      EXPECT_EQ(dims, nullptr);
      return;
    }

    EXPECT_EQ(num_dims, expected_shape->size());
    for (size_t i = 0; i < num_dims; ++i) {
      EXPECT_EQ(dims[i], (*expected_shape)[i]);
    }

    for (int i = 0; i < n; ++i) {
      result.push_back(i + begin);
    }
    return result;
  };

  // We only support the mhlo.dot style input layouts, i.e.:
  //   lhs: [batch, other dims, contract dims]
  //   rhs: [batch, contract dims, other dims]
  auto dim_nums = dot.getDotDimensionNumbers();
  int batch_dims_count = dim_nums.getLhsBatchingDimensions().size();
  int contracting_dims_count = dim_nums.getLhsContractingDimensions().size();

// same data buffer.
//
// This call requires that the `from` tensor and the given type and shape (dims
// and num_dims) are "compatible" (i.e. they occupy the same number of bytes).
// Specifically, given from_type_size = TF_DataTypeSize(TF_TensorType(from)):
//
// ShapeElementCount(dims, num_dims) * TF_DataTypeSize(type)
//
// must equal
//
// TF_TensorElementCount(from) * from_type_size
//

  PartialTensorShape shape;
  TF_RETURN_IF_ERROR(inputs[0]->Shape(&shape));
  if (shape.dims() != 2) {
    return errors::InvalidArgument(
        "Tensor expected to have rank 2 found rank: ", shape.dims());
  }
  int64_t dim_sizes[] = {2, 4};
  for (int i = 0; i < shape.dims(); i++) {
    if (shape.dim_size(i) != dim_sizes[i]) {
      return errors::InvalidArgument("Dim ", i, " expected to be of size ",