y = array_ops.placeholder(dtypes.float32, name='y_hold')
  math_ops.matmul(x, y, name='x_y_prod')


def tfmatmulandadd(_):
  # This tests multiple outputs.
  x = array_ops.placeholder(dtypes.float32, name='x_hold')
  y = array_ops.placeholder(dtypes.float32, name='y_hold')
  math_ops.matmul(x, y, name='x_y_prod')
  math_ops.add(x, y, name='x_y_sum')


def tffunction(_):

    %cst_0 = "tf.Const"() {value = dense<[-1, 10]> : tensor<2xi32>} : () -> tensor<2xi32>
    %1 = "tf.MatMul"(%arg0, %arg1) {
      transpose_a = false, transpose_b = false
    } : (tensor<1x10xf32>, tensor<10x10xf32>) -> tensor<1x10xf32>  loc(fused["MatMul:", "test_opt_out"])
    %2 = "tf.Reshape"(%1, %cst_0) : (tensor<1x10xf32>, tensor<2xi32>) -> tensor<?x10xf32> loc(fused["Reshape:", "model/reshape"])

}

// CHECK: func @cast_bf16_matmul_to_fp32
// CHECK-DAG: %[[cst:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<10x2xf32>}> : () -> tensor<10x2xf32>
// CHECK: %[[matmul:.*]] = "tf.MatMul"(%arg0, %[[cst]])
// CHECK: %[[identity:.*]] = "tf.IdentityN"(%[[matmul]])
// CHECK: return %[[identity]] : tensor<1x2xf32>

func.func @cast_bf16_depthwise_conv_to_fp32(%arg0: tensor<1x3x4x3xf32>) -> (tensor<1x2x2x6xf32>) {

// CHECK:    return
//
// CHECK:  func.func @_ifrt_program__func(%arg0: tensor<1x3xf32>, %arg1: tensor<3x1xf32>) -> tensor<1x1xf32>
// CHECK-SAME:      tfrt_ifrt_serving.program_id = [[PROGRAM_ID]] : i64
// CHECK-NEXT:     %0 = "tf.MatMul"(%arg0, %arg1)
// CHECK:          return

        tf_saved_model.exported_names = ["serving_default"]
    }
  {
    %0 = "tf.ReadVariableOp"(%arg1) {device = ""} : (tensor<!tf_type.resource<tensor<3x1xi32>>>) -> tensor<3x1xi32>
    %1 = "tf.MatMul"(%arg0, %0) {device = "", transpose_a = false, transpose_b = false} : (tensor<1x3xi32>, tensor<3x1xi32>) -> tensor<1x1xi32>
    func.return %1 : tensor<1x1xi32>
  }
  func.func @predict(

    TFRT attributes are sorted alphabetically, passed in as positional
    attributes to the TFRT kernel, rather than as named attributes.

    Example:
      To run "tf.MatMul" op, which has two boolean attributes,
        1. Set _name = "MatMul"
        2. For each TF attribute, split it into two attributes, one for name of
           the TF attribute, and the other for the type and value of the

${generate} \
  --category=array \
  Identity \
  IdentityN \
  ZerosLike \
  Shape \
  ExpandDims \
  OnesLike

${generate} \
  --category=math \
  Mul \
  Conj \
  AddV2 \
  MatMul \
  Neg \
  Sum \
  Sub \
  Div \
  DivNoNan \
  Exp \
  Sqrt \
  SqrtGrad \
  Log1p

${generate} \
  --category=nn \
  SparseSoftmaxCrossEntropyWithLogits \
  ReluGrad \
  Relu \

    %arg1: tensor<1x3xf32> {tf_saved_model.index_path = [0]})
      -> (tensor<3x3xf32> {tf_saved_model.index_path = []}) {
  // CHECK: {{%.*}} = corert.get_op_handler %arg0 "/device:GPU:0"
  %2 = "tf.MatMul"(%arg0, %arg1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "/device:GPU:0", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>

module attributes {tf.versions = {producer = 179 : i32}} {
  func.func @main(%arg0: tensor<*xf32>, %arg1: tensor<?x19xf32>) -> tensor<?x19xf32> {
    %0 = "tf.MatMul"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", transpose_a = false, transpose_b = false} : (tensor<*xf32>, tensor<?x19xf32>) -> tensor<?x19xf32>
    func.return %0 : tensor<?x19xf32>
  }
}

// CHECK-LABEL: HloModule main

namespace mlir {
namespace TF {

// Populate patterns to unroll tf.BatchMatMulV2 op into a sequence of TF ops.
// Since TFLite does not support BatchMatMul operation, it unrolls a BatchMatMul
// op into tf.Reshape, tf.Slice, tf.MatMul, tf.Pack, and tf.Reshape ops.
void PopulateUnrollTfBatchMatMul(MLIRContext* context,
                                 RewritePatternSet& patterns);

}  // namespace TF
}  // namespace mlir