// does various checks while doing so. `input_nodes` will contain the same
// information as input_tensors just in a different structure to make
// following processing easier. TODO(iga): Simplify this nested structure.
Status ProcessInputs(
    const TF_Graph* fn_body, const char* fn_name, int ninputs,
    const TF_Output* inputs, std::vector<OutputTensor>* input_tensors,

  TF_Operation* neg = TF_GraphOperationByName(graph, "imported/neg");
  ASSERT_TRUE(scalar != nullptr);
  ASSERT_TRUE(feed != nullptr);
  ASSERT_TRUE(neg != nullptr);

  // Test basic structure of the imported graph.
  EXPECT_EQ(0, TF_OperationNumInputs(scalar));
  EXPECT_EQ(0, TF_OperationNumInputs(feed));
  ASSERT_EQ(1, TF_OperationNumInputs(neg));
  TF_Output neg_input = TF_OperationInput({neg, 0});

    import.
*   Breaking change to `tf.contrib.data.Dataset` APIs that expect a nested
    structure. Lists are now converted to `tf.Tensor` implicitly. You may need
    to change uses of lists to tuples in existing code. In addition, dicts are
    now supported as a nested structure.

## Changes to contrib APIs

*   Adds tf.contrib.nn.rank_sampled_softmax_loss, a sampled-softmax variant that

package_group(name = "types_allowlist")

# Packages that use StructuredTensors.
# TODO(b/159007891) Remove this package once StructuredTensor is exported.
# LINT.IfChange
package_group(name = "structured_tensor_allowlist")
# LINT.ThenChange(copy.bara.sky)

filegroup(
    name = "intel_binary_blob",
    data = if_mkl_ml(
        [
            "@local_xla//xla/tsl/mkl:intel_binary_blob",
        ],

#ifndef TENSORFLOW_C_EAGER_TAPE_H_
#define TENSORFLOW_C_EAGER_TAPE_H_

// Language-agnostic gradient tape. Does not perform backpropagation, just
// maintains the data structures required to do so.

#include <stack>
#include <unordered_map>
#include <unordered_set>
#include <vector>

#include "tensorflow/core/config/flag_defs.h"
#include "tensorflow/core/config/flags.h"

  // Get theta shape and store in theta_dims.
  int num_dims = TF_NumDims(theta_tensor);
  vector<int64_t> theta_dims(num_dims);
  GetDims(theta_tensor, theta_dims.data());

  // Initialize auxilary data structures.
  vector<float> thetaPlus_data(num_elems);
  vector<float> thetaMinus_data(num_elems);
  AbstractTensorHandle* f_outputs[1];

  // Numerical Grad Check
  for (int i = 0; i < num_elems; i++) {