return op_->SetAttrInt(attr_name, value);
}
Status SetAttrFloat(AbstractOperation* op_, const char* attr_name, float value,
                    ForwardOperation* forward_op_) {
  forward_op_->attrs.Set(attr_name, value);
  return op_->SetAttrFloat(attr_name, value);
}
Status SetAttrBool(AbstractOperation* op_, const char* attr_name, bool value,

  desc->node_builder.Attr(
      attr_name, ArraySlice<const DataType>(
                     reinterpret_cast<const DataType*>(values), num_values));
}

void TF_SetAttrPlaceholder(TF_OperationDescription* desc, const char* attr_name,
                           const char* placeholder) {
  tensorflow::AttrValue attr_value;
  attr_value.set_placeholder(placeholder);
  desc->node_builder.Attr(attr_name, attr_value);
}

		}
		var names []string
		if name == "." {
			attrs, err := listxattr(path)
			if err != nil {
				log.Fatalln(fmt.Errorf("listing attributes failed with: %v", err))
			}
			names = append(names, attrs...)
		} else {
			names = append(names, name)
		}
		var data [][]string
		for _, attr := range names {
			value, err := getxattr(path, attr)
			if err != nil {

  virtual void GetNameAttrList(
      tensorflow::NameAttrList* name_and_attrs) const = 0;

  virtual bool GetInt(absl::string_view, int64_t* result) const = 0;
  virtual bool GetFloat(absl::string_view attr_name, float* result) const = 0;
  virtual bool GetBool(absl::string_view attr_name, bool* result) const = 0;
  virtual bool GetType(absl::string_view attr_name, DataType* result) const = 0;
  virtual Status GetTypeList(

  virtual Status SetAttrString(const char* attr_name, const char* data,
                               size_t length) = 0;
  virtual Status SetAttrInt(const char* attr_name, int64_t value) = 0;
  virtual Status SetAttrFloat(const char* attr_name, float value) = 0;
  virtual Status SetAttrBool(const char* attr_name, bool value) = 0;
  virtual Status SetAttrType(const char* attr_name, DataType value) = 0;

idation-allowing"]+=",float")):h["data-validation-allowing"]+=",float,negative"}if(e.attr("pattern")&&(b.push("custom"),h["data-validation-regexp"]=e.attr("pattern")),e.attr("maxlength")&&(b.push("length"),h["data-validation-length"]="max"+e.attr("maxlength")),!c&&e.attr("list")){var l=[],m=a("#"+e.attr("list"));if(m.find("option").each(function(){l.push(a(this).text())}),0===l.length){var n=a.trim(a("#"+e.attr("list")).text()).split("\n");a.each(n,function(b,c){l.push(a.trim(c))})}m.remove(),a....

  forward_op_.attrs.Set(attr_name, value);
  return parent_op_->SetAttrFloat(attr_name, value);
}
Status TapeOperation::SetAttrBool(const char* attr_name, bool value) {
  forward_op_.attrs.Set(attr_name, value);
  return parent_op_->SetAttrBool(attr_name, value);
}
Status TapeOperation::SetAttrType(const char* attr_name, DataType value) {
  forward_op_.attrs.Set(attr_name, value);

            <td>{{ branch.diff.files_metadata | filter(attr='file', list=platform.subprojects) | map(attr='additions') | sum }}</td>
            <td>{{ ((branch.diff.files_metadata | filter(attr='file', list=platform.subprojects) | map(attr='additions') | sum) / (changes.additions + changes.deletions) * 100) | round(2) }}%</td>
            <td>{{ branch.diff.files_metadata | filter(attr='file', list=platform.subprojects) | map(attr='deletions') | sum }}</td>

==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_TFE_OP_ATTRS_INTERNAL_H_
#define TENSORFLOW_C_EAGER_TFE_OP_ATTRS_INTERNAL_H_

#include "tensorflow/c/conversion_macros.h"
#include "tensorflow/c/eager/abstract_op_attrs.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/core/framework/attr_value.pb.h"

// An equivalent of a tensorflow::NameAttrList protocol buffer, but used in ways

  bool found_value = false;
  for (const auto& attr : node_def.attr()) {
    if (attr.first == "dtype") {
      if (attr.second.type() == tensorflow::DT_INT32) {
        found_dtype = true;
      } else {
        return false;
      }
    } else if (attr.first == "value") {
      if (attr.second.has_tensor() &&
          attr.second.tensor().int_val_size() == 1 &&
          attr.second.tensor().int_val(0) == v) {