code: def conv(
      inputs,
      kernel,
      strides=1,
      padding="valid",
      data_format=None,
      dilation_rate=1,
  ):
      inputs = convert_to_tensor(inputs)
      kernel = convert_to_tensor(kernel)
      num_spatial_dims = inputs.ndim - 2
      strides = standardize_tuple(strides, num_spatial_dims, "strides")
  
      data_format = standardize_data_format(data_format)
      if data_format == "channels_last":
          inputs = _transpose_spatial_inputs(inputs)
      # Transpose kernel from keras format to torch format.
      kernel = _transpose_conv_kernel(kernel)
      if padding == "same" and any(
          d != 1 for d in tree.flatten(strides)
      ):
          # Torch does not support this case in conv2d().
          # Manually pad the tensor.
          inputs, padding = _apply_same_padding(
              inputs,
              kernel.shape[2:],
              strides,
              operation_type="conv",
              dilation_rate=dilation_rate,
          )
      channels = inputs.shape[1]
      kernel_in_channels = kernel.shape[1]
      if channels % kernel_in_channels > 0:
          raise ValueError(
              "The number of input channels must be evenly divisible by "
              f"kernel.shape[1]. Received: inputs.shape={inputs.shape}, "
              f"kernel.shape={kernel.shape}"
          )
      groups = channels // kernel_in_channels
      if num_spatial_dims == 1:
          outputs = tnn.conv1d(
              inputs,
              kernel,
              stride=strides,
              dilation=dilation_rate,
              groups=groups,
              padding=padding,
          )
      elif num_spatial_dims == 2:
          outputs = tnn.conv2d(
              inputs,
              kernel,
              stride=strides,
              dilation=dilation_rate,
              groups=groups,
              padding=padding,
          )
      elif num_spatial_dims == 3:
          outputs = tnn.conv3d(
              inputs,
              kernel,
              stride=strides,
              dilation=dilation_rate,
              groups=groups,
              padding=padding,
          )
      else:
          raise ValueError(
              "Inputs to conv operation should have ndim=3, 4, or 5,"
              "corresponding to 1D, 2D and 3D inputs. Received input "
              f"shape: {inputs.shape}."
          )
  
      if data_format == "channels_last":
          outputs = _transpose_spatial_outputs(outputs)
      return outputs
  
imports: import torch
  import torch.nn.functional
  import tree
  from keras.backend import standardize_data_format
  from keras.backend import standardize_dtype
  from keras.backend.common.backend_utils import compute_conv_transpose_padding_args_for_torch
  from keras.backend.config import epsilon
  from keras.backend.torch.core import cast
  from keras.backend.torch.core import convert_to_tensor
  from keras.backend.torch.core import get_device
  from keras.backend.torch.numpy import expand_dims
  from keras.backend.torch.numpy import maximum
  from keras.backend.torch.numpy import where
  from keras.utils.argument_validation import standardize_tuple
label_desc: Use image node_id 24 for calling a global function with example usage: conv(inputs, kernel, strides, padding, data_format, dilation_rate) and returns: outputs