**1. Installing **TensorFlow-GPU** (TF) prebuilt binaries**
   
   
   Make sure you are using compatible TF and CUDA versions.
   Please refer following TF version and CUDA version compatibility table.
   
   | TF  | CUDA |
   
   | :-------------: | :-------------: |
   
   | 2.5.0  | 11.2 |
   
   | 2.4.0  | 11.0 |
   
   | 2.1.0 - 2.3.0  | 10.1 |
   
   | 1.13.1 - 2.0  | 10.0  |
   

    tags = [
        "no_cuda_asan",  # TODO(b/181771536)
        "no_windows",  # TODO(b/155444728)
    ],
    # We must ensure that the dependencies can be dynamically linked since
    # the shared library must be able to use core:framework.
    deps = [
        ":c_api",
        ":c_api_internal",
        ":c_test_util",
        ":test_op_kernel",
        ":tf_buffer",
        ":tf_buffer_internal",

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

  return OkStatus();
}

// Validates the operations supplied by the plugin.
//
// Uses the 4 simpler `ValidateHelper(const TF_...*)` to validate each
// individual function table and then checks that the function table for a
// specific file type exists if the plugin offers support for creating that
// type of files.
static Status ValidateOperations(const TF_FilesystemPluginOps* ops) {

# inside this directory. TF Mac builds need ~150 GB of disk space to be able to
# run all the tests. Since TFCI Mac VMs execute Bazel test commands in a
# partition with insufficient storage, we specify the
# 'TFCI_MACOS_BAZEL_TEST_DIR_PATH' environment variable to point to a partition
# with ample storage. When this variable is empty (i.e by default), Bazel will
# use the output base directory to run tests.

#include "tensorflow/core/common_runtime/eager/attr_builder.h"

namespace tensorflow {
namespace gradients {

// =============== Experimental C++ API for computing gradients ===============

// Sample gradient function:
//
// class AddGradientFunction : public GradientFunction {
//  public:
//   Status Compute(Context* ctx,
//                  absl::Span<AbstractTensorHandle* const> grad_inputs,

your code interacts with the filesystem, network, etc. and uses checkpointed
variables as part of those interactions (ex: using a string variable to build a
filesystem path), a maliciously created checkpoint might be able to change the
targets of those operations, which could result in arbitrary
read/write/executions.

### Running a TensorFlow server

TensorFlow is a platform for distributed computing, and as such there is a

    vocabulary file.
*   Added `index_to_string_table` which returns a lookup table that maps indices
    to strings.
*   Add `string_to_index_table`, which returns a lookup table that matches
    strings to indices.
*   Add a `ParallelForWithWorkerId` function.
*   Add `string_to_index_table`, which returns a lookup table that matches
    strings to indices.

      tensorflow::unwrap(h);
  // TODO(b/175427838): It would be nice to be able to use tensorflow::isa here.
  if (tensorflow::CustomDeviceTensorHandle::classof(unwrapped_handle)) {
    return tensorflow::down_cast<tensorflow::CustomDeviceTensorHandle*>(
               unwrapped_handle)
        ->DevicePointer();
  }
  // TODO(b/175427838): It would be nice to be able to use tensorflow::isa here.

# configure script unconditionally); otherwise if it is false or undefined, the
# build is static and TensorFlow symbols (in Python only) are loaded into the
# global symbol table in order to support op registration. This means that
# projects building with Bazel and importing TensorFlow as a dependency will not
# depend on libtensorflow_framework.so unless they opt in.
tf_cc_shared_library(