Unfortunately, Helm provides us (1) and (3) together (as .Values), making it hard to insert (2).

However, we can workaround this by placing all of (1) under a specific key (.Values.defaults).
We can then merge the profile onto the defaults, then the user settings onto that.
Finally, we can set all of that under .Values so the chart behaves without awareness.
*/}}
{{- $defaults := $.Values.defaults }}
{{- $_ := unset $.Values "defaults" }}
{{- $profile := dict }}

Unfortunately, Helm provides us (1) and (3) together (as .Values), making it hard to insert (2).

However, we can workaround this by placing all of (1) under a specific key (.Values.defaults).
We can then merge the profile onto the defaults, then the user settings onto that.
Finally, we can set all of that under .Values so the chart behaves without awareness.
*/}}
{{- $defaults := $.Values.defaults }}
{{- $_ := unset $.Values "defaults" }}
{{- $profile := dict }}

Unfortunately, Helm provides us (1) and (3) together (as .Values), making it hard to insert (2).

However, we can workaround this by placing all of (1) under a specific key (.Values.defaults).
We can then merge the profile onto the defaults, then the user settings onto that.
Finally, we can set all of that under .Values so the chart behaves without awareness.
*/}}
{{- $defaults := $.Values.defaults }}
{{- $_ := unset $.Values "defaults" }}
{{- $profile := dict }}

Unfortunately, Helm provides us (1) and (3) together (as .Values), making it hard to insert (2).

However, we can workaround this by placing all of (1) under a specific key (.Values.defaults).
We can then merge the profile onto the defaults, then the user settings onto that.
Finally, we can set all of that under .Values so the chart behaves without awareness.
*/}}
{{- $defaults := $.Values.defaults }}
{{- $_ := unset $.Values "defaults" }}
{{- $profile := dict }}

Unfortunately, Helm provides us (1) and (3) together (as .Values), making it hard to insert (2).

However, we can workaround this by placing all of (1) under a specific key (.Values.defaults).
We can then merge the profile onto the defaults, then the user settings onto that.
Finally, we can set all of that under .Values so the chart behaves without awareness.
*/}}
{{- $defaults := $.Values.defaults }}
{{- $_ := unset $.Values "defaults" }}
{{- $profile := dict }}

    const auto attr = DenseIntElementsAttr::get(shape_type, values);
    return builder.create<TF::ConstOp>(loc, attr);
  }

  const auto type = RankedTensorType::get(shape, builder.getF32Type());
  const auto value_attr = DenseFPElementsAttr::get(type, values);
  return builder.create<TF::ConstOp>(loc, value_attr);
}

// Creates a 1D array with integer/float type.
template <typename T>

public abstract class Message {
    /**
     * Serialize the <code>message</code> onto the provided <code>outputStream</code>, replacing all {@link Throwable}s in the object graph with a placeholder object that can be read back by {@link
     * #receive(java.io.InputStream, ClassLoader)}.
     *
     * @param message object to serialize
     * @param outputSteam stream to serialize onto
     */

  Type resultType = RankedTensorType::get(shape, element_type);
  auto constant_attr = DenseElementsAttr::get(shape_spec_type, shape);
  auto shape_tensor =
      rewriter.create<TF::ConstOp>(loc, shape_spec_type, constant_attr);
  return rewriter.create<TF::ReshapeOp>(loc, resultType, /*tensor=*/value,
                                        /*shape=*/shape_tensor);
}

template <typename BatchMatMulOpType>

}

TF::ConstOp Create1DConstantOp(const std::vector<int>& value, Location loc,
                               OpBuilder* builder) {
  auto type =
      mlir::RankedTensorType::get(value.size(), builder->getIntegerType(32));
  auto dense_values = mlir::DenseIntElementsAttr::get(type, value);
  return builder->create<TF::ConstOp>(loc, dense_values);
}

TF::ConstOp CreateScalarConstantOp(int value, Location loc,

inline bool IsNonConstOp(Operation* op) {
  if (!op) return false;
  if (llvm::isa<arith::ConstantOp, mlir::func::ConstantOp>(op)) return false;
  if (op->hasTrait<OpTrait::ConstantLike>()) return false;
  if (llvm::isa<TFL::ConstOp, TFL::QConstOp>(op)) return false;
  return true;
}

// Returns true if 'op' is a terminator op, otherwise false.
bool IsTerminatorOp(Operation* op);