/*transpose_b=*/op.getAdjY());
    matmuls.emplace_back(matmul.getProduct());
  }

  // Combine the result of each individual MatMul into a rank-3 tensor.
  Type packed_type = RankedTensorType::get(
      {bcast.output_batch_size(), rows, cols}, element_type);
  const auto axis = rewriter.getI64IntegerAttr(0);
  auto pack_op =

Christian Sigg <******@****.***> 1714640622 -0700

                                   "gradients/MatMul", false, true);
    TF_Operation* matmul2 = MatMul(expected_graph_, s_, const0, const3,
                                   "gradients/MatMul_1", true, false);
    expected_grad_outputs[0] = {matmul1, 0};
    expected_grad_outputs[1] = {matmul2, 0};
  }

  TF_Tensor* FloatTensor2x2(const float* values) {

# RUN: tf_tfl_translate -unfold_batchmatmul=false -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=2,5,3:3,7 -tf-input-data-types=DT_FLOAT,DT_FLOAT -tf-output-arrays=MatMul -output-mlir %s -o - 2>&1 | FileCheck %s

node {
  name: "Placeholder"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        dim {
          size: 2

    }
    // FusedMatMul kernel does not support grad_a/grad_b attrs
    if ((matmul->hasAttr("grad_a") &&
         mlir::cast<BoolAttr>(matmul->getAttr("grad_a")).getValue()) ||
        (matmul->hasAttr("grad_b") &&
         mlir::cast<BoolAttr>(matmul->getAttr("grad_b")).getValue())) {
      (void)rewriter.notifyMatchFailure(matmul, [&](Diagnostic &diag) {
        diag << "FusedMatMul kernel does not support grad_a/grad_b attrs";

// RUN: tf-quant-opt %s -split-input-file -quant-lift-quantizable-spots-as-functions -quant-prepare-quantize-drq -quant-quantize='weight-quantization=true' -verify-each=false | FileCheck %s

// -----

module {
  func.func @matmul(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>) {
    %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32>

# RUN: tf_tfl_translate -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=2,5,3:3,7 -tf-input-data-types=DT_FLOAT,DT_FLOAT -tf-output-arrays=MatMul -unfold_batchmatmul=true -output-mlir %s -o - 2>&1 | FileCheck %s

node {
  name: "Placeholder"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        dim {
          size: 2
        }

  // CHECK: tfrt_fallback_async.executeop key(2) cost({{.*}}) device("/device:CPU:0") "tf.MatMul"
  %0 = "tf.ReadVariableOp"(%arg1) {device = "/device:CPU:0", dtype = f32} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32>
  %1 = "tf.MatMul"(%arg0, %0) {T = f32, device = "/device:CPU:0", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>

// CHECK-NEXT:    [[TENSOR:%.*]] = "tf_mlrt.tf_await"([[FURTURE]]) : (!mlrt.future) -> tensor<3x1xf32>
// CHECK-NEXT:    "tf.MatMul"(%arg0, [[TENSOR]]) : (tensor<1x3xf32>, tensor<3x1xf32>) -> tensor<1x1xf32>

        out = math_ops.matmul(input_tensor, self.filters, name='sample/matmul')
        if bias_fn is not None:
          out = bias_fn(out, self.bias)
        if activation_fn is not None:
          out = activation_fn(out)
        return {'output': out}

    model = MatmulModel(weight_shape)
    saved_model_save.save(
        model,
        saved_model_path,
        signatures=model.matmul.get_concrete_function(