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] => 'bool' );

eval {# TF v2.10.0
$ffi->attach(  [ 'SetShape' => 'SetShape' ] =>
	[
		arg 'TF_Tensor' => 'self',
		arg 'tf_dims_buffer'   => [ qw(dims num_dims) ],
	]
	=> 'void'
);
};

$ffi->attach( [  'TensorBitcastFrom' => 'BitcastFrom' ] => [
	arg TF_Tensor => 'from',
	arg TF_DataType => 'type',
	arg TF_Tensor => 'to',
	arg 'tf_dims_buffer'   => [ qw(new_dims num_new_dims) ],
	arg TF_Status => 'status',
] => 'void' );

#### Array helpers ####
use FFI::C::ArrayDef;
use FFI::C::StructDef;
my $adef = FFI::C::ArrayDef->new(
	$ffi,
	name => 'TF_Tensor_array',
	members => [
		FFI::C::StructDef->new(
			$ffi,
			members => [
				p => 'opaque'
			]
		)
	],
);
sub _adef {
	$adef;
}
sub _as_array {
	my $class = shift;
	my $array = $class->_adef->create(0 + @_);
	for my $idx (0..@_-1) {
		next unless defined $_[$idx];
		$array->[$idx]->p($ffi->cast('TF_Tensor', 'opaque', $_[$idx]));
	}
	$array;
}
sub _from_array {
	my ($class, $array) = @_;
	return [
		map {
			$ffi->cast(
				'opaque',
				'TF_Tensor',
				$array->[$_]->p)
		} 0.. $array->count - 1
	]
}

#### Data::Printer ####
sub _data_printer {
	my ($self, $ddp) = @_;

	my @data = (
		[ Type => $ddp->maybe_colorize( $self->Type, 'class' ), ],
		[ Dims =>  sprintf "%s %s %s",
				$ddp->maybe_colorize('[', 'brackets'),
				join(" ",
					map $ddp->maybe_colorize( $self->Dim($_), 'number' ),
						0..$self->NumDims-1),
				$ddp->maybe_colorize(']', 'brackets'),
		],
		[ NumDims => $ddp->maybe_colorize( $self->NumDims, 'number' ), ],
		[ ElementCount => $ddp->maybe_colorize( $self->ElementCount, 'number' ), ],
	);

	my $output;

	$output .= $ddp->maybe_colorize(ref $self, 'class' );
	$output .= ' ' . $ddp->maybe_colorize('{', 'brackets');
	$ddp->indent;
	for my $item (@data) {
		$output .= $ddp->newline;
		$output .= join " ",
			$ddp->maybe_colorize(sprintf("%-15s", $item->[0]), 'hash'),
			$item->[1];
	}
	$ddp->outdent;
	$output .= $ddp->newline;
	$output .= $ddp->maybe_colorize('}', 'brackets');

	return $output;
}

1;

__END__

=pod

=encoding UTF-8

=head1 NAME

AI::TensorFlow::Libtensorflow::Tensor - A multi-dimensional array of elements of a single data type

=head1 SYNOPSIS

  use aliased 'AI::TensorFlow::Libtensorflow::Tensor' => 'Tensor';
  use AI::TensorFlow::Libtensorflow::DataType qw(FLOAT);
  use List::Util qw(product);

  my $dims = [3, 3];

  # Allocate a 3 by 3 ndarray of type FLOAT
  my $t = Tensor->Allocate(FLOAT, $dims);
  is $t->ByteSize, product(FLOAT->Size, @$dims), 'correct size';

  my $scalar_dims = [];
  my $scalar_t = Tensor->Allocate(FLOAT, $scalar_dims);
  is $scalar_t->ElementCount, 1, 'single element';
  is $scalar_t->ByteSize, FLOAT->Size, 'single FLOAT';

=head1 DESCRIPTION

A C<TFTensor> is an object that contains values of a
single type arranged in an n-dimensional array.

For types other than L<STRING|AI::TensorFlow::Libtensorflow::DataType/STRING>,
the data buffer is stored in L<row major order|https://en.wikipedia.org/wiki/Row-_and_column-major_order>.

Of note, this is different from the definition of I<tensor> used in
mathematics and physics which can also be represented as a
multi-dimensional array in some cases, but these tensors are
defined not by the representation but by how they transform. For
more on this see

=over 4

Lim, L.-H. (2021). L<Tensors in computations|https://galton.uchicago.edu/~lekheng/work/acta.pdf>.
Acta Numerica, 30, 555–764. Cambridge University Press.
DOI: L<https://doi.org/10.1017/S0962492921000076>.

=back