# by whitespace. The flag "-F<x>" changes the behavior such that fields are now strings
# separated by the character <x>. Therefore, -F\' (escaped single quote) means that field
# $2 in <Tensor'flow'> is <flow>. This is useful for reading string literals like below.
export TF_VER_SUFFIX=$(awk -F\" '/#define TF_VERSION_SUFFIX/ {print $2}' tensorflow/core/public/version.h)

        "//tensorflow/cc/saved_model:tag_constants",
        "//tensorflow/compiler/jit",
        "//tensorflow/core:array_ops_op_lib",
        "//tensorflow/core:bitwise_ops_op_lib",
        "//tensorflow/core:control_flow_ops_op_lib",
        "//tensorflow/core:core_cpu_internal",
        "//tensorflow/core:direct_session",
        "//tensorflow/core:framework",
        "//tensorflow/core:framework_internal",

      immediate_execution_ctx_.reset(ctx_raw);
    }

    // Computing numerical gradients with TensorFloat-32 is numerically
    // unstable. Some forward pass tests also fail with TensorFloat-32 due to
    // low tolerances
    enable_tensor_float_32_execution(false);
  }

  AbstractContextPtr immediate_execution_ctx_;
  GradientRegistry registry_;
  Status status_;

 public:

*   EagerTensor now supports numpy buffer interface for tensors.
*   Add toggles `tf.enable_control_flow_v2()` and `tf.disable_control_flow_v2()`
    for enabling/disabling v2 control flow.
*   Enable v2 control flow as part of `tf.enable_v2_behavior()` and
    `TF2_BEHAVIOR=1`.
*   AutoGraph translates Python control flow into TensorFlow expressions,
    allowing users to write regular Python inside `tf.function`-decorated

  if (TF_GetCode(status) != TF_OK) return nullptr;
  if (parallel_tensor->num_tensors() == 1) {
    // Copy-off for single-device tensors is allowed to make debugging dynamic
    // control flow easier.
    return TFE_TensorHandleCopySharingTensor(parallel_tensor->tensor(0),
                                             status);
  } else {
    TF_SetStatus(
        status, TF_UNIMPLEMENTED,

    disable the sharding so that all the test outputs are in one file. However,
    it may slow down the tests for not running in parallel and may cause the
    test to timeout but it could be useful when you need to execute a single
    test or more in general your filtered/selected tests have a very low
    execution time and the sharding

      immediate_execution_ctx_.reset(ctx_raw);
    }

    // Computing numerical gradients with TensorFloat-32 is numerically
    // unstable. Some forward pass tests also fail with TensorFloat-32 due to
    // low tolerances
    enable_tensor_float_32_execution(false);
  }

  AbstractContextPtr immediate_execution_ctx_;
  GradientRegistry registry_;
  Status status_;

 public:

      ctx_.reset(ctx_raw);
    }

    // Computing numerical gradients with TensorFloat-32 is numerically
    // unstable. Some forward pass tests also fail with TensorFloat-32 due to
    // low tolerances
    enable_tensor_float_32_execution(false);
  }

  AbstractContextPtr ctx_;

 public:
  bool UseMlir() const { return strcmp(std::get<0>(GetParam()), "mlir") == 0; }

functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

The security impact of skipping those checks should be low, since the attack
scenario would require a malicious user to be able to control the model which as
stated above is already equivalent to code execution. In any case, the

  // TF eager runtime.
  // TODO(b/172877902): These helper functions are currently used to support
  // PyFuncOp on TFRT, and might be useful for ops that directly use low
  // level TF APIs. Remove/replace the following functions when TFRT native
  // ops are implemented.
  //===--------------------------------------------------------------------===//