text: // Copyright (C) 2018-2021 Intel Corporation
  // SPDX-License-Identifier: Apache-2.0
  //
  
  #pragma once
  
  #include "ngraph/axis_vector.hpp"
  #include "ngraph/node.hpp"
  #include "ngraph/op/op.hpp"
  #include "ngraph/runtime/host_tensor.hpp"
  
  namespace ngraph
  {
      namespace op
      {
          namespace v1
          {
              /// \brief Tensor dynamic reshape operation.
              ///
              /// "Converts" an input tensor into a new shape with the same number of elements.
              /// This op does not touch the actual data. If needed, use Transpose for that purpose.
              ///
              class NGRAPH_API Reshape : public Op
              {
              public:
                  NGRAPH_RTTI_DECLARATION;
                  Reshape() = default;
                  /// \brief Constructs a dynamic reshape operation. This operation does not perform
                  ///        transpose.
                  ///
                  /// \param arg The tensor to be reshaped.
                  /// \param shape_pattern The node that defines output shape shape_pattern.
                  ///        If the input shape is \f$(a_0,\dots,a_{k-1})\f$ then the output shape
                  ///        must
                  ///        be of the form \f$(b_0,\dots,b_{j-1})\f$ where \f$\Pi(a_i) = \Pi(b_i)\f$.
                  ///        A value of -1 is allowed for at most one dimension, in which case the
                  ///        dimension size is inferred based on element count of input tensor.
                  /// \param special_zero Treats zeros in `shape_pattern` as wildcard flags indicating
                  /// a
                  ///        copy from input shape at the same index.
                  ///
                  Reshape(const Output<Node>& arg,
                          const Output<Node>& shape_pattern,
                          bool special_zero);
  
                  bool visit_attributes(AttributeVisitor& visitor) override;
                  void validate_and_infer_types() override;
  
                  size_t get_version() const override { return 1; }
                  virtual std::shared_ptr<Node>
                      clone_with_new_inputs(const OutputVector& new_args) const override;
  
                  bool get_special_zero() const { return m_special_zero; }
                  void set_special_zero(bool special_zero) { m_special_zero = special_zero; }
                  bool evaluate(const HostTensorVector& outputs,
                                const HostTensorVector& inputs) const override;
                  bool has_evaluate() const override;
                  bool evaluate_lower(const HostTensorVector& outputs) const override;
                  bool evaluate_upper(const HostTensorVector& outputs) const override;
                  bool constant_fold(OutputVector& output_values,
                                     const OutputVector& inputs_values) override;
  
              protected:
                  bool m_special_zero;
                  bool evaluate_reshape(const HostTensorVector& outputs,
                                        const HostTensorVector& inputs) const;
              };
          } // namespace v1
      }     // namespace op
  } // namespace ngraph
  