if cfg.ExperimentErr != nil {
			base.Fatal(cfg.ExperimentErr)
		}
	}

	// Set environment (GOOS, GOARCH, etc) explicitly.
	// In theory all the commands we invoke should have
	// the same default computation of these as we do,
	// but in practice there might be skew
	// This makes sure we all agree.
	cfg.OrigEnv = toolchain.FilterEnv(os.Environ())
	cfg.CmdEnv = envcmd.MkEnv()
	for _, env := range cfg.CmdEnv {

// AssignVariableOps.
std::unique_ptr<OperationPass<ModuleOp>> CreateUnfreezeConstantsPass();

// Creates a pass that duplicates constants that affect the shape of a tensor
// after some computation.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateDuplicateShapeDeterminingConstantsPass();

// Creates a pass that creates a RestoreV2 op in the initializer function with

  se::Stream* stream =
      ctx->op_device_context() ? ctx->op_device_context()->stream() : nullptr;
  Allocator* allocator = ctx->device()->GetAllocator({});

  // Computation output should always be a tuple.
  VLOG(2) << "Result tuple shape: " << output.on_host_shape().DebugString();
  VLOG(2) << "Result tuple shape (on device): "
          << output.on_device_shape().DebugString();

    std::vector<xla::ExecutionInput> execution_inputs,
    xla::ExecutableRunOptions run_options, xla::LocalExecutable* executable,
    OpKernelContext* ctx, se::DeviceMemoryAllocator* allocator) {
  VLOG(2) << "Executing Xla Computation.";
  Env* env = Env::Default();
  auto start_time = env->NowMicros();

  se::Stream* stream = GetStream(ctx);
  run_options.set_stream(GetStream(ctx));
  run_options.set_allocator(allocator);

			}
		}

	case *Union:
		terms, copied := subst.termlist(t.terms)
		if copied {
			// term list substitution may introduce duplicate terms (unlikely but possible).
			// This is ok; lazy type set computation will determine the actual type set
			// in normal form.
			return &Union{terms}
		}

	case *Interface:
		methods, mcopied := subst.funcList(t.methods)
		embeddeds, ecopied := subst.typeList(t.embeddeds)

			}
		}

	case *Union:
		terms, copied := subst.termlist(t.terms)
		if copied {
			// term list substitution may introduce duplicate terms (unlikely but possible).
			// This is ok; lazy type set computation will determine the actual type set
			// in normal form.
			return &Union{terms}
		}

	case *Interface:
		methods, mcopied := subst.funcList(t.methods)
		embeddeds, ecopied := subst.typeList(t.embeddeds)

  // Internal tensors are those that do not flow in or out of the operation,
  // but instead are part of internal computation. As such, the operation's
  // implementation may manage its memory more efficiently. They are needed
  // however (i.e. not just an implementation detail) since they are part of the
  // computation, which may require relevant metadata such as quantization
  // parameters.
  intermediates:[int];
}

//
// Computing gradient of the loss with respect to features gives us,
//
//  backprop = (Exp(features) / Sum(Exp(features))) - labels
//  backprop = Softmax(features) - labels
//
// Computation of the reduction axis for the Sum op depends on whether the
// input is a scalar or not. Restrict pattern to ranked inputs so that input to
// the Sum op is also ranked.

//   - How are requests for this priority level limited?
//   - What should be done with requests that exceed the limit?
type LimitedPriorityLevelConfiguration struct {
	// `nominalConcurrencyShares` (NCS) contributes to the computation of the
	// NominalConcurrencyLimit (NominalCL) of this level.
	// This is the number of execution seats available at this priority level.
	// This is used both for requests dispatched from this priority level

		for i, c := range cases {
			if c.setFlags != nil || c.inFilePath != "" {
				writeInjectionSettings(t, fmt.Sprintf("%s.%d", c.in, i), c.setFlags, c.inFilePath)
			}
		}
	}
	// Preload default settings. Computation here is expensive, so this speeds the tests up substantially
	defaultTemplate, defaultValues, defaultMesh := readInjectionSettings(t, "default")
	for i, c := range cases {
		i, c := i, c