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,

  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) {

    // file attribute encoding
    static final int ATTR_READONLY   = 0x01;
    static final int ATTR_HIDDEN     = 0x02;
    static final int ATTR_SYSTEM     = 0x04;
    static final int ATTR_VOLUME     = 0x08;
    static final int ATTR_DIRECTORY  = 0x10;
    static final int ATTR_ARCHIVE    = 0x20;

    // extended file attribute encoding(others same as above)
    static final int ATTR_COMPRESSED = 0x800;

  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;

  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);

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....

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

        new_field.key_field = create_cloned_field(  # type: ignore[attr-defined]
            field.key_field,  # type: ignore[attr-defined]
            cloned_types=cloned_types,
        )
    new_field.validators = field.validators  # type: ignore[attr-defined]
    new_field.pre_validators = field.pre_validators  # type: ignore[attr-defined]
    new_field.post_validators = field.post_validators  # type: ignore[attr-defined]

  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);
}

            <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>