// a copy, returns the destination device of the copy. Note that the returned
// device name is not always the device holding the tensor handle's memory. If
// you want the latter, use TFE_TensorHandleBackingDeviceName. This function
// will block till the operation that produces `h` has completed.
TF_CAPI_EXPORT extern const char* TFE_TensorHandleDeviceName(
    TFE_TensorHandle* h, TF_Status* status);

//                 (i.e. `noutputs`) or be null. In the former case,
//                 the names should match the regular expression for ArgDef
//                 names - "[a-z][a-z0-9_]*". In the latter case,
//                 names for outputs will be generated automatically.
//  opts - various options for the function, e.g. XLA's inlining control.
//  description - optional human-readable description of this function.

  //
  // Like GradientTape::RecordOperation, this method takes the operation type
  // `op_type` (e.g. "Add"), the operation's inputs (`input_tensors`,
  // `input_tensor_id`, and `input_dtypes`; the latter two are somewhat
  // redundant but taken as arguments to avoid repeatedly fetching these values
  // between calls to ShouldRecord and Accumulator), and its outputs
  // (`output_tensors`).
  //

# This is the official list of TensorFlow authors for copyright purposes.
# This file is distinct from the CONTRIBUTORS files.
# See the latter for an explanation.

# Names should be added to this file as:
# Name or Organization <email address>
# The email address is not required for organizations.

Google Inc.
Yuan Tang <******@****.***>

    visibility = ["//visibility:public"],
)

# Sometimes Bazel reports darwin_x86_64 as "darwin" and sometimes as
# "darwin_x86_64". The former shows up when building on a Mac x86_64 host for a Mac x86_64 target.
# The latter shows up when cross-compiling for Mac x86_64 from a Mac ARM machine and in internal
# Google builds.
config_setting(
    name = "macos_x86_64_default",
    constraint_values = if_google(

* `tf.ones`, `tf.zeros`, `tf.fill`, `tf.ones_like`, `tf.zeros_like` now take an additional Layout argument that controls the output layout of their results.

* `tf.nest` and `tf.data` now support user defined classes implementing `__tf_flatten__` and `__tf_unflatten__` methods. See [nest_util code examples](https://github.com/tensorflow/tensorflow/blob/04869b4e63bfc03cb13627b3e1b879fdd0f69e34/tensorflow/python/util/nest_util.py#L97)
for an example.