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tfp.experimental.distributions.marginal_fns.ps.cast

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Casts a tensor to a new type.

The operation casts x (in case of Tensor) or x.values (in case of SparseTensor or IndexedSlices) to dtype.

For example:

x = tf.constant([1.8, 2.2], dtype=tf.float32)
tf.cast(x, tf.int32)
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([1, 2], dtype=int32)>

Notice tf.cast has an alias tf.dtypes.cast:

x = tf.constant([1.8, 2.2], dtype=tf.float32)
tf.dtypes.cast(x, tf.int32)
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([1, 2], dtype=int32)>

The operation supports data types (for x and dtype) of uint8, uint16, uint32, uint64, int8, int16, int32, int64, float16, float32, float64, complex64, complex128, bfloat16. In case of casting from complex types (complex64, complex128) to real types, only the real part of x is returned. In case of casting from real types to complex types (complex64, complex128), the imaginary part of the returned value is set to 0. The handling of complex types here matches the behavior of numpy.

Note casting nan and inf values to integral types has undefined behavior.

Note this operation can lead to a loss of precision when converting native Python float and complex variables to tf.float64 or tf.complex128 tensors, since the input is first converted to the float32 data type and then widened. It is recommended to use tf.convert_to_tensor instead of tf.cast for any non-tensor inputs.

x A Tensor or SparseTensor or IndexedSlices of numeric type. It could be uint8, uint16, uint32, uint64, int8, int16, int32, int64, float16, float32, float64, complex64, complex128, bfloat16.
dtype The destination type. The list of supported dtypes is the same as x.
name A name for the operation (optional).

A Tensor or SparseTensor or IndexedSlices with same shape as x and same type as dtype.

TypeError If x cannot be cast to the dtype.