Computes a ND convolution given (N+1+batch_dims)D input
and (N+2)D filter
tensors.
tf.conv(
input: Annotated[Any, TV_Conv_T],
filter: Annotated[Any, TV_Conv_T],
strides,
padding: str,
explicit_paddings=[],
data_format: str = 'CHANNELS_LAST',
dilations=[],
batch_dims: int = 1,
groups: int = 1,
name=None
) > Annotated[Any, TV_Conv_T]
General function for computing a ND convolution. It is required that
1 <= N <= 3
.
Args 
input

A Tensor . Must be one of the following types: half , bfloat16 , float32 , float64 , int32 .
Tensor of type T and shape batch_shape + spatial_shape + [in_channels] in the
case that channels_last_format = true or shape
batch_shape + [in_channels] + spatial_shape if channels_last_format = false .
spatial_shape is Ndimensional with N=2 or N=3 .
Also note that batch_shape is dictated by the parameter batch_dims
and defaults to 1.

filter

A Tensor . Must have the same type as input .
An (N+2)D Tensor with the same type as input and shape
spatial_filter_shape + [in_channels, out_channels] , where spatial_filter_shape
is Ndimensional with N=2 or N=3 .

strides

A list of ints .
1D tensor of length N+2 . The stride of the sliding window for each
dimension of input . Must have strides[0] = strides[N+1] = 1 .

padding

A string from: "SAME", "VALID", "EXPLICIT" .
The type of padding algorithm to use.

explicit_paddings

An optional list of ints . Defaults to [] .
If padding is "EXPLICIT" , the list of explicit padding amounts. For the ith
dimension, the amount of padding inserted before and after the dimension is
explicit_paddings[2 * i] and explicit_paddings[2 * i + 1] , respectively. If
padding is not "EXPLICIT" , explicit_paddings must be empty.

data_format

An optional string from: "CHANNELS_FIRST", "CHANNELS_LAST" . Defaults to "CHANNELS_LAST" .
Used to set the data format. By default CHANNELS_FIRST , uses
NHWC (2D) / NDHWC (3D) or if CHANNELS_LAST , uses NCHW (2D) / NCDHW (3D) .

dilations

An optional list of ints . Defaults to [] .
1D tensor of length N+2 . The dilation factor for each dimension of
input . If set to k > 1 , there will be k1 skipped cells between each
filter element on that dimension. The dimension order is determined by the
value of channels_last_format , see above for details. Dilations in the batch
and depth dimensions must be 1.

batch_dims

An optional int . Defaults to 1 .
A positive integer specifying the number of batch dimensions for the input
tensor. Should be less than the rank of the input tensor.

groups

An optional int . Defaults to 1 .
A positive integer specifying the number of groups in which the input is split
along the channel axis. Each group is convolved separately with
filters / groups filters. The output is the concatenation of all the groups
results along the channel axis. Input channels and filters must both be
divisible by groups.

name

A name for the operation (optional).

Returns 
A Tensor . Has the same type as input .
