# https://github.com/tensorflow/tensorflow/issues/48919.
# Users can still include debug info for a specific kernel, e.g. with:
#     --config=dbg --per_file_copt=+tensorflow/core/kernels/identity_op.*@-g
# Since this .bazelrc file is synced between the tensorflow/tensorflow repo and
# the openxla/xla repo, also include debug info for files under xla/.
build:dbg --per_file_copt=+.*,-tensorflow.*,-xla.*@-g0

			}

			if results, ok := joinIdentityMap[utils.ToStringKey(fieldValues...)]; ok {
				joinKey := utils.ToStringKey(joinFieldValues...)
				identityMap[joinKey] = append(identityMap[joinKey], results...)
			}
		}

		_, foreignValues = schema.GetIdentityFieldValuesMap(tx.Statement.Context, joinResults, joinRelForeignFields)
	} else {
		for _, ref := range rel.References {

                                   val_bcast.getType(), block_arg))) {
    return failure();
  }

  OpBuilder before_cluster_builder(cluster);
  IdentityOp assigned_id = before_cluster_builder.create<IdentityOp>(
      val_bcast.getLoc(), block_arg.getType(), block_arg);
  assigned_id->setAttr(kICIWeightDistributionMlirBridgeMarker,
                       before_cluster_builder.getBoolAttr(true));

DatasetInput GetDatasetInput(Value value) {
  // TODO(haoliang): add an interface for DatasetOp to avoid the following
  // enumeration.
  // Iteratively tracing upwards if parent op is `IdentityOp` or `IdentityNOp`.
  while (
      llvm::isa_and_nonnull<IdentityOp, IdentityNOp>(value.getDefiningOp())) {
    value = value.getDefiningOp()->getOperand(
        mlir::cast<OpResult>(value).getResultNumber());
  }

    Value value = nullptr;
    for (auto v : op.getComponents()) {
      value = v;
    }
    if (!value) return failure();
    rewriter.replaceOpWithNewOp<TF::IdentityOp>(op, value.getType(), value);
    return success();
  }
};

class HandleOptionalGet : public OpRewritePattern<TF::OptionalGetValueOp> {
 public:
  using OpRewritePattern::OpRewritePattern;

  }
};

struct ConvertIdentity : public OpConversionPattern<TF::IdentityOp> {
  using OpConversionPattern::OpConversionPattern;

  LogicalResult matchAndRewrite(
      TF::IdentityOp op, OpAdaptor adaptor,
      ConversionPatternRewriter &rewriter) const override {
    Value input = adaptor.getOperands()[0];
    rewriter.replaceOpWithNewOp<TF::IdentityOp>(
        op, input.getType(), adaptor.getOperands(), op->getAttrs());

          tf_executor.yield %identity : tensor<i32>
        }
        %island_1_result, %island_1_control = tf_executor.island {
          %identity_n:2 = "tf.IdentityN"(%arg1, %island_0_result) : (tensor<i32>, tensor<i32>) -> (tensor<i32>, tensor<i32>)
          tf_executor.yield %identity_n#0
        }
        tf_executor.fetch %island_0_result, %island_1_result : tensor<i32>, tensor<i32>
      }

    }
    return success();
  }
};

// Bypasses IdentityN op.
class ConvertIdentityNOp : public OpRewritePattern<TF::IdentityNOp> {
 public:
  using OpRewritePattern<TF::IdentityNOp>::OpRewritePattern;
  LogicalResult matchAndRewrite(TF::IdentityNOp op,
                                PatternRewriter &rewriter) const override {
    rewriter.replaceOp(op, op.getOperands());
    return success();

such that dx = g(dy). In Python,

```python
with tf.get_default_graph().gradient_override_map(
    {'IdentityN': 'OverrideGradientWithG'}):
  y, _ = identity_n([f(x), x])

@tf.RegisterGradient('OverrideGradientWithG')
def ApplyG(op, dy, _):
  return [None, g(dy)]  # Do not backprop to f(x).
```
  }];

  let arguments = (ins