940 lines
30 KiB
Python
940 lines
30 KiB
Python
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"""Python wrappers around TensorFlow ops.
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This file is MACHINE GENERATED! Do not edit.
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"""
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import collections
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from tensorflow.python import pywrap_tfe as pywrap_tfe
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from tensorflow.python.eager import context as _context
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from tensorflow.python.eager import core as _core
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from tensorflow.python.eager import execute as _execute
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from tensorflow.python.framework import dtypes as _dtypes
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from tensorflow.python.framework import op_def_registry as _op_def_registry
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from tensorflow.python.framework import ops as _ops
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from tensorflow.python.framework import op_def_library as _op_def_library
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from tensorflow.python.util.deprecation import deprecated_endpoints
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from tensorflow.python.util import dispatch as _dispatch
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from tensorflow.python.util.tf_export import tf_export
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from typing import TypeVar
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def bessel_i0(x, name=None):
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r"""TODO: add doc.
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Args:
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x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
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name: A name for the operation (optional).
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Returns:
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A `Tensor`. Has the same type as `x`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "BesselI0", name, x)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return bessel_i0_eager_fallback(
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x, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"BesselI0", x=x, name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("T", _op._get_attr_type("T"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"BesselI0", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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BesselI0 = tf_export("raw_ops.BesselI0")(_ops.to_raw_op(bessel_i0))
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def bessel_i0_eager_fallback(x, name, ctx):
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_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
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_inputs_flat = [x]
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_attrs = ("T", _attr_T)
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_result = _execute.execute(b"BesselI0", 1, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"BesselI0", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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def bessel_i0e(x, name=None):
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r"""TODO: add doc.
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Args:
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x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
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name: A name for the operation (optional).
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Returns:
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A `Tensor`. Has the same type as `x`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "BesselI0e", name, x)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return bessel_i0e_eager_fallback(
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x, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"BesselI0e", x=x, name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("T", _op._get_attr_type("T"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"BesselI0e", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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BesselI0e = tf_export("raw_ops.BesselI0e")(_ops.to_raw_op(bessel_i0e))
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def bessel_i0e_eager_fallback(x, name, ctx):
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_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
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_inputs_flat = [x]
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_attrs = ("T", _attr_T)
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_result = _execute.execute(b"BesselI0e", 1, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"BesselI0e", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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def bessel_i1(x, name=None):
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r"""TODO: add doc.
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Args:
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x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
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name: A name for the operation (optional).
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Returns:
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A `Tensor`. Has the same type as `x`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "BesselI1", name, x)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return bessel_i1_eager_fallback(
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x, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"BesselI1", x=x, name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("T", _op._get_attr_type("T"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"BesselI1", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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BesselI1 = tf_export("raw_ops.BesselI1")(_ops.to_raw_op(bessel_i1))
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def bessel_i1_eager_fallback(x, name, ctx):
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_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
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_inputs_flat = [x]
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_attrs = ("T", _attr_T)
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_result = _execute.execute(b"BesselI1", 1, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"BesselI1", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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def bessel_i1e(x, name=None):
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r"""TODO: add doc.
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Args:
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x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
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name: A name for the operation (optional).
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Returns:
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A `Tensor`. Has the same type as `x`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "BesselI1e", name, x)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return bessel_i1e_eager_fallback(
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x, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"BesselI1e", x=x, name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("T", _op._get_attr_type("T"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"BesselI1e", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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BesselI1e = tf_export("raw_ops.BesselI1e")(_ops.to_raw_op(bessel_i1e))
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def bessel_i1e_eager_fallback(x, name, ctx):
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_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
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_inputs_flat = [x]
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_attrs = ("T", _attr_T)
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_result = _execute.execute(b"BesselI1e", 1, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"BesselI1e", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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def bessel_j0(x, name=None):
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r"""TODO: add doc.
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Args:
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x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
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name: A name for the operation (optional).
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Returns:
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A `Tensor`. Has the same type as `x`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "BesselJ0", name, x)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return bessel_j0_eager_fallback(
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x, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"BesselJ0", x=x, name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("T", _op._get_attr_type("T"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"BesselJ0", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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BesselJ0 = tf_export("raw_ops.BesselJ0")(_ops.to_raw_op(bessel_j0))
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def bessel_j0_eager_fallback(x, name, ctx):
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_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
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_inputs_flat = [x]
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_attrs = ("T", _attr_T)
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_result = _execute.execute(b"BesselJ0", 1, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"BesselJ0", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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def bessel_j1(x, name=None):
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r"""TODO: add doc.
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Args:
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x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
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name: A name for the operation (optional).
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Returns:
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A `Tensor`. Has the same type as `x`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "BesselJ1", name, x)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return bessel_j1_eager_fallback(
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x, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"BesselJ1", x=x, name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("T", _op._get_attr_type("T"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"BesselJ1", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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BesselJ1 = tf_export("raw_ops.BesselJ1")(_ops.to_raw_op(bessel_j1))
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def bessel_j1_eager_fallback(x, name, ctx):
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_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
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_inputs_flat = [x]
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_attrs = ("T", _attr_T)
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_result = _execute.execute(b"BesselJ1", 1, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"BesselJ1", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def bessel_k0(x, name=None):
|
||
|
|
r"""TODO: add doc.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `x`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "BesselK0", name, x)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return bessel_k0_eager_fallback(
|
||
|
|
x, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"BesselK0", x=x, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"BesselK0", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
BesselK0 = tf_export("raw_ops.BesselK0")(_ops.to_raw_op(bessel_k0))
|
||
|
|
|
||
|
|
|
||
|
|
def bessel_k0_eager_fallback(x, name, ctx):
|
||
|
|
_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
|
||
|
|
_inputs_flat = [x]
|
||
|
|
_attrs = ("T", _attr_T)
|
||
|
|
_result = _execute.execute(b"BesselK0", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"BesselK0", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def bessel_k0e(x, name=None):
|
||
|
|
r"""TODO: add doc.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `x`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "BesselK0e", name, x)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return bessel_k0e_eager_fallback(
|
||
|
|
x, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"BesselK0e", x=x, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"BesselK0e", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
BesselK0e = tf_export("raw_ops.BesselK0e")(_ops.to_raw_op(bessel_k0e))
|
||
|
|
|
||
|
|
|
||
|
|
def bessel_k0e_eager_fallback(x, name, ctx):
|
||
|
|
_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
|
||
|
|
_inputs_flat = [x]
|
||
|
|
_attrs = ("T", _attr_T)
|
||
|
|
_result = _execute.execute(b"BesselK0e", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"BesselK0e", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def bessel_k1(x, name=None):
|
||
|
|
r"""TODO: add doc.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `x`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "BesselK1", name, x)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return bessel_k1_eager_fallback(
|
||
|
|
x, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"BesselK1", x=x, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"BesselK1", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
BesselK1 = tf_export("raw_ops.BesselK1")(_ops.to_raw_op(bessel_k1))
|
||
|
|
|
||
|
|
|
||
|
|
def bessel_k1_eager_fallback(x, name, ctx):
|
||
|
|
_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
|
||
|
|
_inputs_flat = [x]
|
||
|
|
_attrs = ("T", _attr_T)
|
||
|
|
_result = _execute.execute(b"BesselK1", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"BesselK1", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def bessel_k1e(x, name=None):
|
||
|
|
r"""TODO: add doc.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `x`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "BesselK1e", name, x)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return bessel_k1e_eager_fallback(
|
||
|
|
x, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"BesselK1e", x=x, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"BesselK1e", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
BesselK1e = tf_export("raw_ops.BesselK1e")(_ops.to_raw_op(bessel_k1e))
|
||
|
|
|
||
|
|
|
||
|
|
def bessel_k1e_eager_fallback(x, name, ctx):
|
||
|
|
_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
|
||
|
|
_inputs_flat = [x]
|
||
|
|
_attrs = ("T", _attr_T)
|
||
|
|
_result = _execute.execute(b"BesselK1e", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"BesselK1e", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def bessel_y0(x, name=None):
|
||
|
|
r"""TODO: add doc.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `x`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "BesselY0", name, x)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return bessel_y0_eager_fallback(
|
||
|
|
x, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"BesselY0", x=x, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"BesselY0", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
BesselY0 = tf_export("raw_ops.BesselY0")(_ops.to_raw_op(bessel_y0))
|
||
|
|
|
||
|
|
|
||
|
|
def bessel_y0_eager_fallback(x, name, ctx):
|
||
|
|
_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
|
||
|
|
_inputs_flat = [x]
|
||
|
|
_attrs = ("T", _attr_T)
|
||
|
|
_result = _execute.execute(b"BesselY0", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"BesselY0", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def bessel_y1(x, name=None):
|
||
|
|
r"""TODO: add doc.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `x`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "BesselY1", name, x)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return bessel_y1_eager_fallback(
|
||
|
|
x, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"BesselY1", x=x, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"BesselY1", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
BesselY1 = tf_export("raw_ops.BesselY1")(_ops.to_raw_op(bessel_y1))
|
||
|
|
|
||
|
|
|
||
|
|
def bessel_y1_eager_fallback(x, name, ctx):
|
||
|
|
_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
|
||
|
|
_inputs_flat = [x]
|
||
|
|
_attrs = ("T", _attr_T)
|
||
|
|
_result = _execute.execute(b"BesselY1", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"BesselY1", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def dawsn(x, name=None):
|
||
|
|
r"""TODO: add doc.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `x`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "Dawsn", name, x)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return dawsn_eager_fallback(
|
||
|
|
x, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"Dawsn", x=x, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"Dawsn", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
Dawsn = tf_export("raw_ops.Dawsn")(_ops.to_raw_op(dawsn))
|
||
|
|
|
||
|
|
|
||
|
|
def dawsn_eager_fallback(x, name, ctx):
|
||
|
|
_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
|
||
|
|
_inputs_flat = [x]
|
||
|
|
_attrs = ("T", _attr_T)
|
||
|
|
_result = _execute.execute(b"Dawsn", 1, inputs=_inputs_flat, attrs=_attrs,
|
||
|
|
ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"Dawsn", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def expint(x, name=None):
|
||
|
|
r"""TODO: add doc.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `x`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "Expint", name, x)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return expint_eager_fallback(
|
||
|
|
x, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"Expint", x=x, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"Expint", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
Expint = tf_export("raw_ops.Expint")(_ops.to_raw_op(expint))
|
||
|
|
|
||
|
|
|
||
|
|
def expint_eager_fallback(x, name, ctx):
|
||
|
|
_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
|
||
|
|
_inputs_flat = [x]
|
||
|
|
_attrs = ("T", _attr_T)
|
||
|
|
_result = _execute.execute(b"Expint", 1, inputs=_inputs_flat, attrs=_attrs,
|
||
|
|
ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"Expint", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def fresnel_cos(x, name=None):
|
||
|
|
r"""TODO: add doc.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `x`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "FresnelCos", name, x)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return fresnel_cos_eager_fallback(
|
||
|
|
x, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"FresnelCos", x=x, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"FresnelCos", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
FresnelCos = tf_export("raw_ops.FresnelCos")(_ops.to_raw_op(fresnel_cos))
|
||
|
|
|
||
|
|
|
||
|
|
def fresnel_cos_eager_fallback(x, name, ctx):
|
||
|
|
_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
|
||
|
|
_inputs_flat = [x]
|
||
|
|
_attrs = ("T", _attr_T)
|
||
|
|
_result = _execute.execute(b"FresnelCos", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"FresnelCos", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def fresnel_sin(x, name=None):
|
||
|
|
r"""TODO: add doc.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `x`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "FresnelSin", name, x)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return fresnel_sin_eager_fallback(
|
||
|
|
x, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"FresnelSin", x=x, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"FresnelSin", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
FresnelSin = tf_export("raw_ops.FresnelSin")(_ops.to_raw_op(fresnel_sin))
|
||
|
|
|
||
|
|
|
||
|
|
def fresnel_sin_eager_fallback(x, name, ctx):
|
||
|
|
_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
|
||
|
|
_inputs_flat = [x]
|
||
|
|
_attrs = ("T", _attr_T)
|
||
|
|
_result = _execute.execute(b"FresnelSin", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"FresnelSin", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
def spence(x, name=None):
|
||
|
|
r"""TODO: add doc.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `x`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "Spence", name, x)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return spence_eager_fallback(
|
||
|
|
x, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"Spence", x=x, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"Spence", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
Spence = tf_export("raw_ops.Spence")(_ops.to_raw_op(spence))
|
||
|
|
|
||
|
|
|
||
|
|
def spence_eager_fallback(x, name, ctx):
|
||
|
|
_attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
|
||
|
|
_inputs_flat = [x]
|
||
|
|
_attrs = ("T", _attr_T)
|
||
|
|
_result = _execute.execute(b"Spence", 1, inputs=_inputs_flat, attrs=_attrs,
|
||
|
|
ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"Spence", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|