}
  }
}

}  // namespace

void TransposeOp::getCanonicalizationPatterns(RewritePatternSet& results,
                                              MLIRContext* context) {
  results.add<ConvertTransposeToDecreaseRank>(context);
}

OpFoldResult TransposeOp::fold(FoldAdaptor adaptor) {
  auto operands = adaptor.getOperands();

                IllegalArgumentException.class, () -> systemSessionFactory.newRepositorySession(request));
        assertEquals(
                "Unknown resolver transport 'illegal'. Supported transports are: wagon, apache, jdk, auto",
                exception.getMessage());
        properties.remove("maven.resolver.transport");
    }

    @Test
    void versionFilteringTest() throws InvalidRepositoryException {

    if (const auto adj_y = op->getAttrOfType<BoolAttr>("adj_y")) {
      is_supported_affine_op = !adj_y.getValue();
    }
  } else if (llvm::isa<TF::MatMulOp>(op)) {
    if (const auto adj_y = op->getAttrOfType<BoolAttr>("transpose_b")) {
      is_supported_affine_op = !adj_y.getValue();
    }
  }

  if (!is_supported_affine_op) return failure();

  // Bind input, output and weight to the given values.

// CHECK-NEXT: return %0 : tensor<2x3x2x2xi32>
// CHECK-NEXT:}

func.func @transpose(%arg0: tensor<2x3x2xi32>) -> tensor<2x3x2xi32> {
  %0 = "vhlo.transpose_v1"(%arg0) <{permutation = #vhlo.tensor_v1<dense<[2, 1, 0]> : tensor<3xi64>>}> : (tensor<2x3x2xi32>) -> tensor<2x3x2xi32>
  return %0 : tensor<2x3x2xi32>
}

// CHECK:func.func private @transpose(%arg0: tensor<2x3x2xi32>) -> tensor<2x3x2xi32> {

        // [{X, Y}, {Size, Z}], the better clustering is Size with Y because the
        // output of size will be a small tensor while Y is a potentially large
        // tensor that must be computed and possible transposed/copied before
        // the second cluster executes.
        Node* n = GetOnlyNodeIn(*to);
        bool is_shape_consumer_op = n && IsShapeConsumerOp(*n);
        if (!is_shape_consumer_op) {
          return false;

  %dq = "tfl.dequantize"(%q) : (tensor<5x2x!quant.uniform<i8<-127:127>:f32, 1.113490e-03>>) -> tensor<5x2xf32>
  %t = "tfl.transpose"(%dq, %perm) : (tensor<5x2xf32>, tensor<2xi32>) -> tensor<2x5xf32>
  func.return %t : tensor<2x5xf32>

  // QDQ: %[[perm:.*]] = arith.constant dense<[1, 0]> : tensor<2xi32>

		n, err := fmt.Sscanf(out, tmpl, &month, &date, &ts, &pid)
		if n != 4 || err != nil {
			t.Errorf("Got %q, does not match template %q (%d %s)", out, tmpl, n, err)
		}
		return
	}

	// Non-UNIX domain transports.
	var parsedHostname, timestamp string
	var pid int
	tmpl := fmt.Sprintf("<%d>%%s %%s syslog_test[%%d]: %s\n", LOG_USER+LOG_INFO, in)
	n, err := fmt.Sscanf(out, tmpl, &timestamp, &parsedHostname, &pid)

  return op.getX();
}

OpFoldResult FoldCancellableTranspose(TransposeOp op) {
  // Operand is a TransposeOp.
  auto transpose = dyn_cast_or_null<TF::TransposeOp>(op.getX().getDefiningOp());
  if (!transpose) return {};

  // If the transpose ops are on different devices, we don't fold them.
  if (transpose->getBlock() != op->getBlock()) {
    tensorflow::DataType dtype;

static bool tpu_use_substreams_for_cross_tpu_device_transfers_flag = true;

// Given a tensor of `shape` and `type`, as what shape should it be stored on
// the TPU device? This function tranposes or flattens the excessively-padded
// tensors to rank 1, but leaves other tensor shapes alone.
absl::StatusOr<xla::Shape> TpuShapeRepresentation(
    const TensorShape& shape, DataType type, bool use_fast_memory,

      CompileTimeConstantOperand<TF::TileOp, 1>,                // $multiples
      CompileTimeConstantOperand<TF::TopKV2Op, 1>,              // $k
      CompileTimeConstantOperand<TF::TransposeOp, 1>,           // $perm
      CompileTimeConstantOperand<TF::TruncatedNormalOp, 0>,     // $shape
      CompileTimeConstantOperand<TF::UnsortedSegmentMaxOp, 2>,  // $num_segments