class_docstr: 1D convolution layer (e.g. temporal convolution).
  
  This layer creates a convolution kernel that is convolved with the layer
  input over a single spatial (or temporal) dimension to produce a tensor of
  outputs. If `use_bias` is True, a bias vector is created and added to the
  outputs. Finally, if `activation` is not `None`, it is applied to the
  outputs as well.
  
  Args:
      filters: int, the dimension of the output space (the number of filters
          in the convolution).
      kernel_size: int or tuple/list of 1 integer, specifying the size of the
          convolution window.
      strides: int or tuple/list of 1 integer, specifying the stride length
          of the convolution. `strides > 1` is incompatible with
          `dilation_rate > 1`.
      padding: string, `"valid"`, `"same"` or `"causal"`(case-insensitive).
          `"valid"` means no padding. `"same"` results in padding evenly to
          the left/right or up/down of the input. When `padding="same"` and
          `strides=1`, the output has the same size as the input.
          `"causal"` results in causal(dilated) convolutions, e.g. `output[t]`
          does not depend on`input[t+1:]`. Useful when modeling temporal data
          where the model should not violate the temporal order.
          See [WaveNet: A Generative Model for Raw Audio, section2.1](
          https://arxiv.org/abs/1609.03499).
      data_format: string, either `"channels_last"` or `"channels_first"`.
          The ordering of the dimensions in the inputs. `"channels_last"`
          corresponds to inputs with shape `(batch, steps, features)`
          while `"channels_first"` corresponds to inputs with shape
          `(batch, features, steps)`. It defaults to the `image_data_format`
          value found in your Keras config file at `~/.keras/keras.json`.
          If you never set it, then it will be `"channels_last"`.
      dilation_rate: int or tuple/list of 1 integers, specifying the dilation
          rate to use for dilated convolution.
      groups: A positive int 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`.
      activation: Activation function. If `None`, no activation is applied.
      use_bias: bool, if `True`, bias will be added to the output.
      kernel_initializer: Initializer for the convolution kernel. If `None`,
          the default initializer (`"glorot_uniform"`) will be used.
      bias_initializer: Initializer for the bias vector. If `None`, the
          default initializer (`"zeros"`) will be used.
      kernel_regularizer: Optional regularizer for the convolution kernel.
      bias_regularizer: Optional regularizer for the bias vector.
      activity_regularizer: Optional regularizer function for the output.
      kernel_constraint: Optional projection function to be applied to the
          kernel after being updated by an `Optimizer` (e.g. used to implement
          norm constraints or value constraints for layer weights). The
          function must take as input the unprojected variable and must return
          the projected variable (which must have the same shape). Constraints
          are not safe to use when doing asynchronous distributed training.
      bias_constraint: Optional projection function to be applied to the
          bias after being updated by an `Optimizer`.
  
  Input shape:
  - If `data_format="channels_last"`:
      A 3D tensor with shape: `(batch_shape, steps, channels)`
  - If `data_format="channels_first"`:
      A 3D tensor with shape: `(batch_shape, channels, steps)`
  
  Output shape:
  - If `data_format="channels_last"`:
      A 3D tensor with shape: `(batch_shape, new_steps, filters)`
  - If `data_format="channels_first"`:
      A 3D tensor with shape: `(batch_shape, filters, new_steps)`
  
  Returns:
      A 3D tensor representing `activation(conv1d(inputs, kernel) + bias)`.
  
  Raises:
      ValueError: when both `strides > 1` and `dilation_rate > 1`.
  
  Examples:
  
  >>> # The inputs are 128-length vectors with 10 timesteps, and the
  >>> # batch size is 4.
  >>> x = np.random.rand(4, 10, 128)
  >>> y = keras.layers.Conv1D(32, 3, activation='relu')(x)
  >>> print(y.shape)
  (4, 8, 32)
code: def _compute_causal_padding(self):
      left_pad = self.dilation_rate[0] * (self.kernel_size[0] - 1)
      if self.data_format == "channels_last":
          causal_padding = [[0, 0], [left_pad, 0], [0, 0]]
      else:
          causal_padding = [[0, 0], [0, 0], [left_pad, 0]]
      return causal_padding
  
file_path: keras/keras/layers/convolutional/conv1d.py
imports: from keras import ops
  from keras.api_export import keras_export
  from keras.layers.convolutional.base_conv import BaseConv
label_desc: Use image node_id 2 for calling the Conv1D obj's underlying member method code with example usage: obj._compute_causal_padding() and returns: causal_padding