See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
// Rewrites computations generated by the xla.compile() Python code into
// XlaLaunch nodes.
//
// xla.compile() does two main things:
// a) marks operators that make up an XLA computation with the attribute

  PopulateAllQuantizablePatterns(ctx, patterns);

  if (failed(applyPatternsAndFoldGreedily(module_op, std::move(patterns)))) {
    // There are cases where no rewrites happen even if a pattern matches,
    // causing this to result in a convergence failure. Consider this as a
    // best-effort.
    module_op.emitWarning("Failed to converge pattern at QuantizePass.");
  }
}

  // graph rewrites. Returns false if the inference_type is DT_FLOAT or
  // `weight_quantization` flag is set.
  bool RunPropagationAndRewriteQuantizationPasses() const {
    return inference_type != tensorflow::DT_FLOAT && !weight_quantization;
  }

  // TODO: b/202075505 - make implicit weight type clearer
  // Whether run the passes and graph rewrites for dynamic range quantization.

  auto new_results =
      GetValueWithToken(builder, llvm::to_vector(terminator->getOperands()),
                        token, rewritten_operands, flatten_tuple);
  terminator->setOperands(new_results);
}

// Rewrites a `mhlo.if` op to receive and forward a `mhlo.token`. As If op does
// not have any operands other than the predicate, hence we implicitly capture
// the parent token. Also we use the same implicit token for use in the If op's

    const std::map<string, int>& host_compute_core, FunctionLibraryRuntime* flr,
    FunctionLibraryDefinition* fld, std::vector<string>* shape_inference_graphs,
    bool* has_outside_compilation);

// Rewrites XLA computation in `clusters` to replace outside compilation nodes
// with XlaHostCompute, and moves those outside compilations into `g`. If shapes
// of outside compilation outputs cannot be determined now, we will store shape

  // flatbuffer file.
  if (auto custom_op = dyn_cast<mlir::TFL::CustomOp>(*op)) {
    return custom_op.getCustomCode().starts_with("Flex");
  }

  // We never see TFL::IfOps in the IR -- it is flatbuffer_export that rewrites
  // them from TF::IfOps.
  if (isa<TF::IfOp>(op)) {
    return false;
  }

  // We assume all other TF operators are flex delegated.
  return op->getDialect()->getNamespace() == "tf";
}

                                 ArrayRef<int64_t> values, Location location) {
  auto cst_attr = rewriter.getI64TensorAttr(values);
  return rewriter.create<TF::ConstOp>(location, cst_attr.getType(), cst_attr);
}

// Rewrites broadcast->reshape to a reshape->broadcast that reduces
// the rank of the input and output of the broadcast.
class SimplifyBroadcastReshape : public OpRewritePattern<BroadcastToOp> {

using ::mlir::stablehlo::ConvDimensionNumbersAttr;

class NchwConvolutionToNhwcPass
    : public impl::NchwConvolutionToNhwcPassBase<NchwConvolutionToNhwcPass> {
 private:
  void runOnOperation() override;
};

// Rewrites NCHW convolution to NHWC.
// * Src dimension numbers: [b, f, 0, 1]x[o, i, 0, 1]->[b, f, 0, 1]
// * Dst dimension numbers: [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f]
class RewriteNchwConvolutionToNhwc

	lit.SetImplicit(true)

	n := Expr(lit)
	if n.Type() == nil {
		base.FatalfAt(pos, "mkdotargslice: typecheck failed")
	}
	return n
}

// FixVariadicCall rewrites calls to variadic functions to use an
// explicit ... argument if one is not already present.
func FixVariadicCall(call *ir.CallExpr) {
	fntype := call.Fun.Type()
	if !fntype.IsVariadic() || call.IsDDD {
		return
	}

			rewriteDir = m.Path + "@" + m.Version + strings.TrimPrefix(importPath, m.Path)
		} else {
			rewriteDir = importPath
		}
		rewrite += a.Package.Dir + "=>" + rewriteDir + ";"
	}

	// Add rewrites for overlays. The 'from' and 'to' paths in overlays don't need to have
	// same basename, so go from the overlay contents file path (passed to the compiler)
	// to the path the disk path would be rewritten to.