// ResourceHandle during Session run where the TF_Tensor is converted to a
// Tensor.
// TFv2 does not depend on this conversion. There is no matching
// TF_TensorFromTensorV1 because the conversion to string is performed by the
// python side of Session.
static Status TF_TensorToTensorV1(const TF_Tensor* src, Tensor* dst) {
  Status status = TF_TensorToTensor(src, dst);
  if (!status.ok()) {
    return status;
  }

    // CHECK: %[[ADD:.*]] = "tf.Add"(%[[MUL]], %[[MUL]]) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
    // CHECK: return %[[ADD]] : tensor<1xf32>
    %0 = "tf.Mul"(%arg0, %arg0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<*xf32>
    %1 = "tf.Add"(%0, %0) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
    func.return %1 : tensor<*xf32>
  }

  // CHECK-LABEL: func @simple_chain_with_broadcast

}

// CHECK-LABEL: testAddN
func.func @testAddN(tensor<? x f32>, tensor<? x f32>, tensor<? x f32>) -> tensor<? x f32> {
^bb0(%arg0: tensor<? x f32>, %arg1: tensor<? x f32>, %arg2: tensor<? x f32>):
  // CHECK: "tfl.add_n"(%arg0, %arg1, %arg2)
  %0 = "tfl.add_n"(%arg0, %arg1, %arg2): (tensor<? x f32>, tensor<? x f32>, tensor<? x f32>) -> tensor<? x f32>
  func.return %0 : tensor<? x f32>
}

// -----

  %2 = "tf.Mul"(%arg1, %arg3) : (tensor<?x1xf32>, tensor<f32>) -> tensor<?x1xf32>
  // Add of a scalar const and a tensor.
  %3 = "tf.AddV2"(%arg2, %1) : (tensor<f32>, tensor<?x1xf32>) -> tensor<?x1xf32>
  %4 = "tf.AddV2"(%arg3, %2) : (tensor<f32>, tensor<?x1xf32>) -> tensor<?x1xf32>

      }
      return %graph#0, %graph#1, %graph#2, %graph#3 : !tf_res, !tf_res, tensor<f32>, tensor<f32>
    }
    ```

    After:

    ```mlir
    func @while_body(%arg0: !tf_res, %arg1: !tf_res, %arg2: tensor<f32>, %arg3: tensor<f32>, %chain_0: tensor<i32>, %chain_1: tensor<i32>) -> (!tf_res, !tf_res, tensor<f32>, tensor<f32>, tensor<i32>, tensor<i32>) {

  let description = [{
    Reverses specific dimensions of a tensor.

    Given a tensor, and a int32/int64 tensor axis representing the set
    of dimensions of tensor to reverse.
    This operation reverses each dimension i for
    which there exists j s.t. axis[j] == i.

    Args:
      tensor: A Tensor. Must be one of the following types:

      *begin != 0 || *end != 1 || *strides != 1)
    return {};

  // First tensor dimension is dynamic.
  auto arg_ty = tensor.getType().dyn_cast<ShapedType>();
  if (!arg_ty || !arg_ty.hasRank() || arg_ty.getNumDynamicDims() != 1 ||
      !arg_ty.isDynamicDim(0))
    return {};

  // Argument tensor rank is equal to the number of packed dimensions.
  if (arg_ty.getRank() != getValues().size()) return {};

    return op.emitOpError("requires min to be a 1d float tensor");

  auto max = GetRankedTensorTypeForOperand(op.getMax());
  if (max && !IsOfRankedFloatTensorType(max, 1))
    return op.emitOpError("requires max to be a 1d float tensor");

  Value inputs = op.getInputs();
  if (!HasRankAtLeast(inputs, 1))
    return op.emitError("requires inputs to be at least 1d float tensor");

  int64_t num_bits = op.getNumBits();

//   %cst = arith.constant dense<1.0> : tensor<16x16x4xf32>
//   %0 = "tfl.conv_2d"...
//   %1 = "tfl.add"(%0, %cst) : (tensor<16x16x4xf32>, tensor<16x16x4xf32>)
// After this optimization:
//   %cst = arith.constant dense<1.0> : tensor<f32>
//   %0 = "tfl.conv_2d"...
//   %1 = "tfl.add"(%0, %cst) : (tensor<16x16x4xf32>, tensor<f32>)
// This pattern can enable more fusing opportunities when the binary op is

// This method will actually merge the information contained in the
// types, it is capable of refining:
//   tensor<!tf_type.variant<tensor<?x8xf32>>>
// and:
//   tensor<!tf_type.variant<tensor<10x?xf32>>>
// into:
//   tensor<!tf_type.variant<tensor<10x8xf32>>>
//
// In case of inconsistencies (rank disagreement for example), it returns `lhs`.
Type TypeMeet(Type lhs, Type rhs) {