-   Check if your changes are consistent with the
    [guidelines](#general-guidelines-and-philosophy-for-contribution).
-   Changes are consistent with the [Coding Style](#c-coding-style).
-   Run the [unit tests](#running-unit-tests).

## How to become a contributor and submit your own code

   
     * If you have above configuration and using _**Windows**_ platform -
       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the %PATH% environment variable.
       * Refer [windows setup guide](https://www.tensorflow.org/install/gpu#windows_setup).
     * If you have above configuration and using _**Ubuntu/Linux**_ platform -
       * Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable.

numpy ~= 1.23.5 ; python_version <= "3.11"
numpy ~= 1.26.0 ; python_version >= "3.12"
wheel ~= 0.41.2
h5py >= 3.10.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0

# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version

// for `Env::Default()` altogether, but that's a different project, not in
// scope for now. I'm just mentioning this here as that transition will mean
// removal of the registration part from `Env` and adding it here instead: we
// will need tables to hold for each scheme the function tables that implement
// the needed functionality instead of the current `FileSystemRegistry` code in
// `core/platform/env.cc`.

  const char* op_name_ = nullptr;
  // TODO(srbs): Use this.
  string device_name_;
};

// GraphContext wraps a TF_Graph modeling a single function and manages the
// "execution" of operation, i.e. adding them to the function.
class GraphContext : public TracingContext {
 public:
  explicit GraphContext(const char* name)
      : TracingContext(kGraph),
        graph_(new TF_Graph(), TF_DeleteGraph),

tf-keras-nightly==2.16.0.dev2024021410 \
    --hash=sha256:05a4c19c6a795ec9ed6f4dac69d443601b9206b910524d8b19bad6e362d49f47 \
    --hash=sha256:83d5a1dd3979b42164a2fac4bdd2ed5981ef0c2a0514ca64e4bc67e369caef5c
    # via tb-nightly
typing-extensions==4.8.0 \
    --hash=sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0 \
    --hash=sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef
    # via -r requirements.in

        MaskedTensor(tf.experimental.ExtensionType): values: tf.Tensor mask:
        tf.Tensor` The `tf.ExtensionType` base class works similarly to
        [`typing.NamedTuple`](https://docs.python.org/3/library/typing.html#typing.NamedTuple)
        and
        [`@dataclasses.dataclass`](https://docs.python.org/3/library/dataclasses.html#dataclasses.dataclass)
        from the standard Python library.

/// this is used, please consult next sections. Here we just wrap these tables
/// in lint warnings so that changes here cause changes to the versioning data
/// as well. Here is a short summary of what changes are allowed:
///   * adding a new method at the end of a table is allowed at any time;
///   * any other change to these tables is only allowed on a major TensorFlow
///     version change (e.g., from 2.x to 3.0). This is provided as an escape

      if (!status->status.ok()) return;
      // TODO(nareshmodi): TFE_OpSetAttrFunction and TFE_OpSetAttrFunctionList
      // require TFE_Op* and just convert it internally a NameAttrValue, so
      // consider adding an overload to the C API to make this case easier.
      TFE_OpSetAttrFunction(op, attr_name, func_op);
      TFE_DeleteOp(func_op);
    } break;
    case tensorflow::AttrValue::kList: {
      // String

    return TF_SetStatus(status, TF_ALREADY_EXISTS, path);
  }

  auto metadata = gcs_file->gcs_client.InsertObject(
      bucket, object, "",
      // Adding this parameter means HTTP_CODE_PRECONDITION_FAILED
      // will be returned if the object already exists, so avoid reuploading.
      gcs::IfGenerationMatch(0), gcs::Fields(""));