// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package gob

import (
	"reflect"
)

`

const arrayHelper = `
func enc%[2]sArray(state *encoderState, v reflect.Value) bool {
	// Can only slice if it is addressable.
	if !v.CanAddr() {
		return false
	}
	return enc%[2]sSlice(state, v.Slice(0, v.Len()))
}
`

const sliceHelper = `

  //
  // 1) `atomic`: the list is treated as a single entity, like a scalar.
  //      Atomic lists will be entirely replaced when updated. This extension
  //      may be used on any type of list (struct, scalar, ...).
  // 2) `set`:
  //      Sets are lists that must not have multiple items with the same value. Each
  //      value must be a scalar, an object with x-kubernetes-map-type `atomic` or an

  // Val tensor must be a scalar or of a shape [1, ... , 1, elements_depth].
  const int elements_rank = elements_shape.size();
  for (int i = 0; i < elements_rank - 1; ++i) {
    if (elements_shape[i] != 1) {
      return false;
    }
  }

  auto elements_depth = elements_shape.empty() ? 1 : elements_shape.back();
  // If elements depth equals 1 (i.e., scalar or tensor with 1 element), then we

  [(CopyAttrs $src, $dest)]>;

// Canonicalize: Log(1.0 + x) to Log1p(x)
//
// We currently do this rewrite only if the constant `1` is a scalar, because
// it is safely broadcastable to any shape. To be able to canonicalize when
// constant values is not a scalar, we have to first prove that it is
// broadcastable to `x`, which requires static shape information.
def LogToLog1p : Pat<
  (TF_LogOp:$src (TF_AddV2Op $arg,

    result.push_back(dt);
  }
  return result;
}

std::vector<std::vector<int64_t>> InterestingShapes() {
  std::vector<std::vector<int64_t>> interesting_shapes;
  interesting_shapes.push_back({});             // Scalar
  interesting_shapes.push_back({10});           // 1D Vector
  interesting_shapes.push_back({3, 3});         // 2D Matrix
  interesting_shapes.push_back({1, 4, 6, 10});  // Higher Dimension Tensor
  return interesting_shapes;

	}

	return mem
}

// Convert scalar OpArg into the proper OpWhateverArg instruction
// Convert scalar OpSelectN into perhaps-differently-indexed OpSelectN
// Convert aggregate OpArg into Make of its parts (which are eventually scalars)
// Convert aggregate OpSelectN into Make of its parts (which are eventually scalars)
// Returns the converted value.
//

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		m.Name = "/sched/gomaxprocs:threads"
		m.Value = Value{kind: ValueUint64, scalar: e.base.args[0]}
	case go122.EvHeapAlloc:
		m.Name = "/memory/classes/heap/objects:bytes"
		m.Value = Value{kind: ValueUint64, scalar: e.base.args[0]}
	case go122.EvHeapGoal:
		m.Name = "/gc/heap/goal:bytes"
		m.Value = Value{kind: ValueUint64, scalar: e.base.args[0]}
	default:

  //
  // 1) `atomic`: the list is treated as a single entity, like a scalar.
  //      Atomic lists will be entirely replaced when updated. This extension
  //      may be used on any type of list (struct, scalar, ...).
  // 2) `set`:
  //      Sets are lists that must not have multiple items with the same value. Each
  //      value must be a scalar, an object with x-kubernetes-map-type `atomic` or an

  return tensorflow::GetTypeFromTFTensorShape({1}, builder.getIntegerType(32));
}

Value ReshapeScalarToSizeType(OpBuilder builder, Value scalar, Location loc) {
  auto size_type = GetSizeType(builder);
  return builder.create<TF::ReshapeOp>(
      loc, ArrayRef<Type>{size_type},
      ArrayRef<Value>{scalar, GetR1Const(size_type.getShape(), builder, loc)});
}

LogicalResult CreateInitBufferValue(ArrayRef<int64_t> element_shape,