std::string translated_name = TranslateName(fname);
  ops_->new_read_only_memory_region_from_file(
      filesystem_.get(), translated_name.c_str(), region.get(),
      plugin_status.get());

  if (TF_GetCode(plugin_status.get()) == TF_OK)
    *result = std::make_unique<ModularReadOnlyMemoryRegion>(
        std::move(region), read_only_memory_region_ops_.get());

  return StatusFromTF_Status(plugin_status.get());
}

  out.resize(block_count, 0);
  // The cache has space for `block_count` blocks. The loop with i = 0 should
  // fill the cache, and the loop with i = 1 should be all cache hits. The
  // fetcher checks that it is called once and only once for each offset (to
  // fetch the corresponding block).
  for (int i = 0; i < 2; i++) {
    for (int j = 0; j < block_count; j++) {
      TF_EXPECT_OK(ReadCache(&cache, "", block_size * j, block_size, &out));
    }

@ruy//
EOF

  license_query "attr('licenses', 'notice', deps($BUILD_TARGET))" > $BATS_TEST_TMPDIR/expected_licenses
  license_query "deps($LICENSES_TARGET)" > $BATS_TEST_TMPDIR/actual_licenses

  # Column 1 is left only, Column 2 is right only, Column 3 is shared lines
  # Select lines unique to actual_licenses, i.e. extra licenses.

//
// Note that TFE_OpGetFlatInputCount and TFE_OpGetFlatInput operate on a flat
// sequence of inputs, unlike TFE_OpGetInputLength (for getting the length of a
// particular named input list, which may only be part of the op's inputs).
TF_CAPI_EXPORT extern int TFE_OpGetFlatInputCount(const TFE_Op* op,
                                                  TF_Status* status);
// Returns a borrowed reference to one of `op`'s inputs. Use

// completed. This is only valid on local TFE_TensorHandles. The pointer
// returned will be on the device in which the TFE_TensorHandle resides (so e.g.
// 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.

Follow either of the two links above to access the appropriate CLA and instructions for how to sign and return it. Once we receive it, we'll be able to accept your pull requests.

***NOTE***: Only original source code from you and other people that have signed the CLA can be accepted into the main repository.

### Contributing code

If you have improvements to TensorFlow, send us your pull requests! For those

};

// Returns the number of elements in the gradient tensor.
int64_t TapeVSpace::NumElements(AbstractTensorHandle* tensor) const {
  // TODO(srbs): It seems like this is used only for performance optimization
  // and not for correctness. The only downside of keeping this 1 seems to be
  // that the gradient accumulation is unbounded and we will never
  // aggressively aggregate accumulated gradients to recover memory.

                                TF_DataType* tf_dtype) {
  switch (dtype.code) {
    case DLDataTypeCode::kDLBool:
      if (dtype.bits != 8) {
        return tensorflow::errors::InvalidArgument(
            "Only DLPack bools of bitwidth 8 are supported, got: ", dtype.bits);
      }
      *tf_dtype = TF_DataType::TF_BOOL;
      return absl::OkStatus();

    case DLDataTypeCode::kDLUInt:
      switch (dtype.bits) {

    deps = [
        ":c_api_macros_hdrs",
        ":tf_attrtype",
        ":tf_buffer_hdrs",
        ":tf_datatype_hdrs",
        ":tf_status_headers",
        ":tf_tensor_hdrs",
        # TODO: Only include tf_tstring_hdrs. Don't expose the implementation of TF_TString to API
        # users.
        ":tf_tstring",
        "//tensorflow/core:protos_all_cc",
    ],
)

tf_cuda_library(

                                TF_WRITABLE_FILE_OPS_ABI, "writable file"));

  if (ops->read_only_memory_region_ops != nullptr)
    TF_RETURN_IF_ERROR(CheckABI(ops->read_only_memory_region_ops_abi,
                                TF_READ_ONLY_MEMORY_REGION_OPS_ABI,
                                "read only memory region"));

  return OkStatus();
}

// Checks if the plugin and core API numbers match, logging mismatches.