status->status = InvalidArgument("Expected rank is ", num_dims,
                                     " but actual rank is ", rank);
    return;
  }
  for (int i = 0; i < num_dims; i++) {
    dims[i] = shape.dim_size(i);
  }
}

int TF_CheckpointReaderGetVariableNumDims(TF_CheckpointReader* reader,
                                          const char* name) {
  const auto& m = reader->GetVariableToShapeMap();

  }
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {
    for (int d = 0; d < num_dims; ++d) {
      proto.add_dim()->set_size(dims[d]);
    }
  }

  forward_op_->attrs.Set(attr_name, proto);
  return op_->SetAttrShape(attr_name, dims, num_dims);
}
Status SetAttrFunction(AbstractOperation* op_, const char* attr_name,

// Information about the shape of a Tensor and its type.
struct TF_ShapeAndType {
  // Number of dimensions. -1 indicates unknown rank.
  int num_dims;
  // Array of dimensions. -1 indicates unknown dim.
  int64_t* dims;
  // The data type. May be 0 to denote unknown type.
  TF_DataType dtype;
};

typedef struct TF_ShapeAndType TF_ShapeAndType;

// A list of TF_ShapeAndType elements..

  virtual AbstractTensorInterface* CreateBoolScalar(bool value) = 0;

  // Tensor creation functions
  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, absl::Span<const int64_t> dim_sizes) = 0;

  typedef void (*MemoryReleaser)(void* data, size_t len, void* arg);

  // Create a tensor instance from the given data buffer and description.

  // shape is explicitly requested where possible.
  int (*num_dims)(void* data, TF_Status* status);

  // Computes the axis length at `dim_index`.
  int64_t (*dim)(void* data, int dim_index, TF_Status* status);

  void (*deallocator)(void* data);

  // Summarizes the value of this tensor. The caller takes ownership of the

  std::vector<int64_t>* stride_arr = &tf_dlm_tensor_ctx->strides;
  shape_arr->resize(ndim);
  stride_arr->resize(ndim, 1);
  for (int i = 0; i < ndim; i++) {
    (*shape_arr)[i] = tensor->dim_size(i);
  }
  for (int i = ndim - 2; i >= 0; --i) {
    (*stride_arr)[i] = (*shape_arr)[i + 1] * (*stride_arr)[i + 1];
  }

  dlm_tensor->dl_tensor.shape = shape_arr->data();

    TF_RETURN_IF_ERROR(operation->SetAttrType("dtype", dtype));
    if (!shape.unknown_rank()) {
      TF_RETURN_IF_ERROR(operation->SetAttrShape(
          "shape", reinterpret_cast<int64_t*>(shape.dim_sizes().data()),
          shape.dims()));
    }
    int num_outputs = 1;
    std::vector<AbstractTensorHandle*> outputs(num_outputs);
    TF_RETURN_IF_ERROR(operation->Execute(

// has registration:
//   REGISTER_OP("Concat")
//       .Input("concat_dim: int32")
//       .Input("values: N * T")
//       .Output("output: T")
//       .Attr("N: int >= 2")
//       .Attr("T: type");
// that defines two inputs, "concat_dim" and "values" (in that order).
// You must use TF_AddInput() for the first input (since it takes a

t-Samhain","An D\xf9bhlachd"],monthsShort:["Faoi","Gear","M\xe0rt","Gibl","C\xe8it","\xd2gmh","Iuch","L\xf9n","Sult","D\xe0mh","Samh","D\xf9bh"],monthsParseExact:!0,weekdays:["Did\xf2mhnaich","Diluain","Dim\xe0irt","Diciadain","Diardaoin","Dihaoine","Disathairne"],weekdaysShort:["Did","Dil","Dim","Dic","Dia","Dih","Dis"],weekdaysMin:["D\xf2","Lu","M\xe0","Ci","Ar","Ha","Sa"],longDateFormat:{LT:"HH:mm",LTS:"HH:mm:ss",L:"DD/MM/YYYY",LL:"D MMMM YYYY",LLL:"D MMMM YYYY HH:mm",LLLL:"dddd, D MMMM YYYY ...

don?ma&him?ye&ragan?tat???r&a&bom?gan?hihci??u&agedos?kas?ustak???s&os?ufomihs??t&amihcay?iran??w&a&g&im&anah?o??omak??kihci?zustum??ihsak??y&agamak?imonihci???e&akas?nagot??i&azni?esohc?h&asa?s&abanuf?ohc???ka&to?zok??musi?orihs?r&akihabihsokoy?o&dim?tak??ukujuk??usihs??nano&hc?yk??o&d&iakustoy?ustam??hsonhot?k&a&rihs?t??iba??nihsaran?sobimanim?tas&arihsimao?imot??uhc?yihcay??u&kujno?s&ayaru?t&imik?tuf???zarasik?????c&cah,ed,?g&as!.&a&gas?m&a&tamah?yik??ihsak??rat?t&a&gatik?hatik??ira!ihsin????...