%cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<512x512xf32>} : () -> tensor<512x512xf32>
  %out_1 = "tf.MatMul"(%arg0, %cst) {
    device = "", transpose_a = false, transpose_b = false
  } : (tensor<1x12x12x512xf32>, tensor<512x512xf32>) -> tensor<*xf32>
  %out_2 = "tf.MatMul"(%arg0, %arg0) {
    device = "", transpose_a = false, transpose_b = true
  } : (tensor<1x12x12x512xf32>, tensor<1x12x12x512xf32>) -> tensor<*xf32>

module {
  // TODO(b/260020937): Support transpose_a, transpose_b for matmul.
  func.func @matmul(%arg0: tensor<2x12xf32>) -> (tensor<*xf32>) {
    %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<12x2xf32>} : () -> tensor<12x2xf32>

  %out_matrix = "tf.MatMul"(%in_matrix, %matrix)  : (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<3x3xf32>
  %in_matrix1 = "tf.TensorArrayReadV3"(%handle_2, %loop_count, %flow_in_2) : (tensor<?x!tf_type.resource>, tensor<i32>, tensor<*xf32>) -> tensor<3x3xf32>
  %out_matrix1 = "tf.MatMul"(%out_matrix, %matrix_2)  : (tensor<3x3xf32>, tensor<3x3xf32>) -> 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>

# CHECK-NEXT: [[R1:%.*]] = "tf.ReadVariableOp"([[ARG1]]) {{{.*}}} : (tensor<!tf_type.resource<tensor<3x5xf32>>>) -> tensor<3x5xf32>
# CHECK-NEXT: [[R2:%.*]] = "tf.MatMul"([[R0]], [[R1]]) <{{{.*}}}> {{{.*}}} : (tensor<5x3xf32>, tensor<3x5xf32>) -> tensor<5x5xf32>


def Test():

  x = tf.compat.v1.get_variable(
      name='x',
      shape=(5, 3),

    %0 = "tf.MatMul"(%arg0, %arg1) {attr_map = "0:transpose_a,1:transpose_a", device = "", transpose_a = false, transpose_b = false} : (tensor<1x2x2x3xf32>, tensor<2x1024xf32>) -> tensor<*xf32>
    return %0 : tensor<*xf32>
  }

// CHECK-LABEL: func @matmul
// CHECK-DAG: %[[CONST:.*]] = arith.constant dense<0.000000e+00> : tensor<2x1024xf32>

# CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["key"]

# CHECK-NEXT: [[R0:%.*]] = "tf.ReadVariableOp"([[ARG1]]) {{{.*}}} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32>
# CHECK-NEXT: [[R1:%.*]] = "tf.MatMul"([[ARG0]], [[R0]]) <{{{.*}}}> {device = ""} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
# CHECK-NEXT: return [[R1]] : tensor<3x3xf32>


def Test():

  x = tf.constant([[1.0], [1.0], [1.0]])

        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(

    %0 = "tf.MatMul"(%arg0, %arg1) {attr_map = "0:transpose_a,1:transpose_a", device = "", transpose_a = false, transpose_b = false} : (tensor<1x2x2x3xf32>, tensor<2x1024xf32>) -> tensor<*xf32>
    return %0 : tensor<*xf32>
  }

// CHECK-LABEL: func @matmul
// CHECK-DAG: %[[CONST:.*]] = arith.constant dense<0.000000e+00> : tensor<2x1024xf32>

// CHECK-LABEL: private @composite_matmul_with_bias_and_relu6_fn_1
// CHECK-NEXT: %[[matmul:.*]] = "tf.MatMul"(%arg0, %arg1)
// CHECK-SAME: attr_map = "0:transpose_a,1:transpose_b"
// CHECK-NEXT: tf.BiasAdd
// CHECK-NEXT: tf.Relu6
// CHECK-NEXT: return

// CHECK-LABEL: private @composite_matmul_with_bias_and_relu_fn_1
// CHECK-NEXT: tf.MatMul"(%arg0, %arg1)
// CHECK-SAME: attr_map = "0:transpose_a,1:transpose_b"