to the core.
*   As every PR requires several CPU/GPU hours of CI testing, we discourage
    submitting PRs to fix one typo, one warning,etc. We recommend fixing the
    same issue at the file level at least (e.g.: fix all typos in a file, fix
    all compiler warnings in a file, etc.)
*   Tests should follow the
    [testing best practices](https://www.tensorflow.org/community/contribute/tests)

        ":tf_file_statistics",
        ":tf_status",
        ":tf_status_helper",
        "//tensorflow/core/platform:env",
        "//tensorflow/core/platform:path",
        "//tensorflow/core/platform:types",
    ],
)

cc_library(
    name = "kernels_hdrs",
    hdrs = [
        "kernels.h",
        "kernels_experimental.h",
    ],
    visibility = ["//tensorflow:internal"],
    deps = [

  // TODO(b/166669239): This is needed to support AttrBuilder::Get for string
  // attributes. Number type attrs and DataType attrs work fine without this.
  // Consider getting rid of this and making the behavior between number types
  // and string consistent.
  forward_op_->attrs.BuildNodeDef();
  std::unique_ptr<GradientFunction> gradient_fn;
  TF_RETURN_IF_ERROR(registry.Lookup(*forward_op_, &gradient_fn));

// for a GPU tensor this will return a pointer to GPU memory). The pointer is
// only guaranteed to be valid until TFE_DeleteTensorHandle is called on this
// TensorHandle. Only supports POD data types.
TF_CAPI_EXPORT extern void* TFE_TensorHandleDevicePointer(TFE_TensorHandle*,
                                                          TF_Status*);

// This function will block till the operation that produces `h` has

#define TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_CONTEXT_H_

#include <functional>
#include <map>
#include <memory>
#include <vector>

#include "absl/types/optional.h"
#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/immediate_execution_distributed_manager.h"
#include "tensorflow/c/eager/immediate_execution_operation.h"

            "//tensorflow/core/profiler/lib:traceme",
            "@com_google_absl//absl/algorithm:container",
            "@com_google_absl//absl/types:span",
            "@com_google_absl//absl/types:variant",
        ],
    }) + [
        ":abstract_tensor_handle",
        "//tensorflow/c:c_api_macros_hdrs",
        "//tensorflow/core:gpu_runtime",

                        : const_cast<TF_Tensor**>(input_tensors.data()),
                    /*input_tensors_as_shapes*/ nullptr,
                    /*input_resource_shapes_and_types*/ nullptr, &output_shapes,
                    /*output_resource_shapes_and_types*/ nullptr, status_);
    CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);
    CHECK_EQ(output_shapes->num_items, 1);

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

#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/platform/types.h"

using tensorflow::dyn_cast;
using tensorflow::string;
using tensorflow::gtl::ArraySlice;

namespace tensorflow {

  TF_AttrType ret = TF_ATTR_INT;
  const tensorflow::AttrTypeMap* attr_types_;
  bool is_function;
  status->status = tensorflow::AttrTypeMapForOp(
      tensorflow::unwrap(op)->Name().c_str(), &attr_types_, &is_function);
  if (!status->status.ok()) {
    return ret;
  }
  status->status =
      tensorflow::AttrTypeByName(*attr_types_, attr_name, &ret, is_list);
  return ret;
}

      transformation or translation of a Source form, including but
      not limited to compiled object code, generated documentation,
      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work