shape_handle, static_cast<DataType>(types[i]));
  }

  ic->set_output_handle_shapes_and_types(output.index, shape_and_type_vec);
}

// Helpers for loading a TensorFlow plugin (a .so file).
Status LoadDynamicLibrary(const char* library_filename, void** result,
                          const void** buf, size_t* len);

    return {};
  }
  const auto& fdef_lib = gdef.library();
  if (fdef_lib.gradient_size() > 0) {
    status->status = tensorflow::errors::Internal(
        "GradientDef is not supported in reading Dataset related functions: ",
        text_proto);
    return {};
  }
  std::vector<UniqueFuncPtr> ret;
  for (const FunctionDef& fdef : fdef_lib.function()) {
    // Make a copy so that we can mutate it.

  EXPECT_TRUE(second_block);
  // Reading the second full block will return 8 bytes, from a cache hit.
  second_block = false;
  TF_EXPECT_OK(ReadCache(&cache, "", block_size, block_size, &out));
  EXPECT_FALSE(second_block);
  EXPECT_EQ(out.size(), file_size - block_size);
}

TEST(RamFileBlockCacheTest, Inconsistent) {
  // Tests the detection of interrupted reads leading to partially filled blocks

    }
    auto begin = data.begin();
    if (offset > pos) {
      // The block begins before the slice we're reading.
      begin += offset - pos;
    }
    auto end = data.end();
    if (pos + data.size() > offset + n) {
      // The block extends past the end of the slice we're reading.
      end -= (pos + data.size()) - (offset + n);
    }
    if (begin < end) {
      size_t bytes_to_copy = end - begin;

//     but plugins didn't provide will be set to `nullptr` values and core
//     TensorFlow will know to not call these on behalf of users;
//   * increased security as plugins will not be able to alter function table
//     after loading up. Thus, malicious plugins can't alter functionality to
//     probe for gadgets inside core TensorFlow. We can even protect the area
//     of memory where the copies reside to not allow any more writes to it