let dependentDialects = ["mlir::mhlo::MhloDialect", "TFL::TensorFlowLiteDialect"];
  let constructor = "mlir::odml::CreateCompositeLoweringPass()";
}

def TransposeCommuteOpsPass : Pass<"transpose-commute-ops", "ModuleOp"> {
  let summary = "Move transpose through specific ops";
  let dependentDialects = ["mlir::stablehlo::StablehloDialect"];
  let constructor = "mlir::odml::CreateTransposeCommuteOpsPass()";

  %1 = "tf.Transpose"(%0, %cst_0): (tensor<1x2xf32>, tensor<2xi32>) -> tensor<2x1xf32>
  %2 = "tf.Transpose"(%0, %cst_0): (tensor<1x2xf32>, tensor<2xi32>) -> tensor<2x1xf32>
  func.return %1, %2 : tensor<2x1xf32>, tensor<2x1xf32>

// CHECK:  %cst = arith.constant
// CHECK:  %[[FQ:.*]] = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2)
// CHECK:  %[[T1:.*]] = "tf.Transpose"(%[[FQ]], %cst)

// point constant.
def : Pat<(TFL_DequantizeOp
             (TFL_QuantizeOp (Arith_ConstantOp F32ElementsAttr:$cst), $qt)),
          (TFL_ConstOp $cst)>;

// Transpose conv supports hybrid computation with quantized weights.
def FoldQuantWeightsIntoTposeConv : Pat<
  (TFL_TransposeConvOp
    $output_shape,
    (TFL_DequantizeOp $quant_weights),
    $quant_input,

    // 262657 = 1 + 512 + 512 * 512
    // expected-remark@+1 {{Cost: 262657}}
    %2 = "tf.MatMul"(%arg, %1) {device = "/job:localhost/replica:0/task:0/device:CPU:0", transpose_a = false, transpose_b = false} : (tensor<?x512xf32>, tensor<512x512xf32>) -> tensor<?x512xf32>
    // expected-remark@+1 {{Cost: 512}}
    func.return %2 : tensor<?x512xf32>
}

// CHECK-LABEL: test_dynamic_shape

  // available in TFLite.
  //
  // - tfl.gather_nd -> tfl.transpose -> tfl.gather_nd -> tfl.transpose
  //   where ...
  //     - all tfl.gather_nd op instances take [0, 0, 1, 1, ..., n-1, n-1] as
  //       the indices arugment,
  //     - first tranpose op takes perm [2, 1, 0, 3], and
  //     - second transpose op take perm [1, 2, 0, 3].
  //

// CHECK:           [[VAL_6:%.*]] = arith.constant dense<[1, 0]> : tensor<2xi32>
// CHECK:           [[VAL_7:%.*]] = "tf.Transpose"([[VAL_1]], [[VAL_6]]) : (tensor<3x4xf32>, tensor<2xi32>) -> tensor<4x3xf32>
// CHECK:           [[VAL_8:%.*]] = arith.constant dense<[1, 0]> : tensor<2xi32>
// CHECK:           [[VAL_9:%.*]] = "tf.Transpose"([[VAL_4]], [[VAL_8]]) : (tensor<1x3xf32>, tensor<2xi32>) -> tensor<3x1xf32>

    %2 = "tf.Cast"(%identity) {Truncate = false} : (tensor<*xi8>) -> tensor<*xi32>
    %3 = "tf.Sub"(%2, %weight_zp) : (tensor<*xi32>, tensor<*xi32>) -> tensor<*xi32>

    %5 = "tf.MatMul"(%1, %3) {
      attr_map = "transpose_a:0,transpose_b:1"
    } : (tensor<*xi32>, tensor<*xi32>) -> tensor<*xi32>
    func.return %5 : tensor<*xi32>
  }

  // Conv2D with int32 accumulation
  func.func private @internal_conv2d_fn(

      %arg1 = "tf.Const"() {value = dense<-3.0> : tensor<11x7xf32>} : () -> tensor<11x7xf32>

      %1 = "tf.MatMul"(%arg0, %arg1) {transpose_a = false, transpose_b = true} : (tensor<5x7xf32>, tensor<11x7xf32>) -> tensor<5x11xf32>

      func.return %1 : tensor<5x11xf32>
    }
  })";
  TF_ASSERT_OK_AND_ASSIGN(
      XlaCompiler::CompilationResult result,

  %1 = "tf.Transpose"(%0, %cst_0): (tensor<1x2xf32>, tensor<2xi32>) -> tensor<2x1xf32>
  %2 = "tf.Transpose"(%0, %cst_0): (tensor<1x2xf32>, tensor<2xi32>) -> tensor<2x1xf32>
  func.return %1, %2 : tensor<2x1xf32>, tensor<2x1xf32>

// CHECK:  %cst = arith.constant
// CHECK:  %[[FQ:.*]] = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2)
// CHECK:  %[[T1:.*]] = "tf.Transpose"(%[[FQ]], %cst)

	resp, err := http.Get("http://example.com/")
	if err != nil {
		// handle error
	}
	defer resp.Body.Close()
	body, err := io.ReadAll(resp.Body)
	// ...

# Clients and Transports

For control over HTTP client headers, redirect policy, and other
settings, create a [Client]:

	client := &http.Client{
		CheckRedirect: redirectPolicyFunc,
	}