tf.raw_ops.Max Computes the maximum of elements across dimensions of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Max
tf.raw_ops.Max(
    input, axis, keep_dims=False, name=None
)
 Reduces input along the dimensions given in axis. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is true, the reduced dimensions are retained with length 1.

 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64, qint8, quint8, qint32, qint16, quint16. The tensor to reduce.
  axis   A Tensor. Must be one of the following types: int32, int64. The dimensions to reduce. Must be in the range [-rank(input), rank(input)).
  keep_dims   An optional bool. Defaults to False. If true, retain reduced dimensions with length 1.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.Maximum Returns the max of x and y (i.e. x > y ? x : y) element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Maximum
tf.raw_ops.Maximum(
    x, y, name=None
)
 Example:
x = tf.constant([0., 0., 0., 0.])
y = tf.constant([-2., 0., 2., 5.])
tf.math.maximum(x, y)
<tf.Tensor: shape=(4,), dtype=float32, numpy=array([0., 0., 2., 5.], dtype=float32)>

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int16, int32, int64.
  y   A Tensor. Must have the same type as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.MaxIntraOpParallelismDataset Creates a dataset that overrides the maximum intra-op parallelism.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MaxIntraOpParallelismDataset
tf.raw_ops.MaxIntraOpParallelismDataset(
    input_dataset, max_intra_op_parallelism, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  max_intra_op_parallelism   A Tensor of type int64. Identifies the maximum intra-op parallelism to use.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.MaxPool Performs max pooling on the input.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPool
tf.raw_ops.MaxPool(
    input, ksize, strides, padding, explicit_paddings=[],
    data_format='NHWC', name=None
)

 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32, float64, int32, int64, uint8, int16, int8, uint16, qint8. 4-D input to pool over.
  ksize   A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.
  strides   A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.
  padding   A string from: "SAME", "VALID", "EXPLICIT". The type of padding algorithm to use.
  explicit_paddings   An optional list of ints. Defaults to [].
  data_format   An optional string from: "NHWC", "NCHW", "NCHW_VECT_C". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.MaxPool3D Performs 3D max pooling on the input.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPool3D
tf.raw_ops.MaxPool3D(
    input, ksize, strides, padding, data_format='NDHWC', name=None
)

 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32. Shape [batch, depth, rows, cols, channels] tensor to pool over.
  ksize   A list of ints that has length >= 5. 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have ksize[0] = ksize[4] = 1.
  strides   A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  data_format   An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.MaxPool3DGrad Computes gradients of 3D max pooling function.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPool3DGrad
tf.raw_ops.MaxPool3DGrad(
    orig_input, orig_output, grad, ksize, strides, padding,
    data_format='NDHWC', name=None
)

 Args
  orig_input   A Tensor. Must be one of the following types: half, bfloat16, float32. The original input tensor.
  orig_output   A Tensor. Must have the same type as orig_input. The original output tensor.
  grad   A Tensor. Must be one of the following types: half, bfloat16, float32. Output backprop of shape [batch, depth, rows, cols, channels].
  ksize   A list of ints that has length >= 5. 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have ksize[0] = ksize[4] = 1.
  strides   A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  data_format   An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as grad.


tf.raw_ops.MaxPool3DGradGrad Computes second-order gradients of the maxpooling function.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPool3DGradGrad
tf.raw_ops.MaxPool3DGradGrad(
    orig_input, orig_output, grad, ksize, strides, padding,
    data_format='NDHWC', name=None
)

 Args
  orig_input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The original input tensor.
  orig_output   A Tensor. Must have the same type as orig_input. The original output tensor.
  grad   A Tensor. Must have the same type as orig_input. Output backprop of shape [batch, depth, rows, cols, channels].
  ksize   A list of ints that has length >= 5. 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have ksize[0] = ksize[4] = 1.
  strides   A list of ints that has length >= 5. 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  data_format   An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as orig_input.


tf.raw_ops.MaxPoolGrad Computes gradients of the maxpooling function.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPoolGrad
tf.raw_ops.MaxPoolGrad(
    orig_input, orig_output, grad, ksize, strides, padding, explicit_paddings=[],
    data_format='NHWC', name=None
)

 Args
  orig_input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The original input tensor.
  orig_output   A Tensor. Must have the same type as orig_input. The original output tensor.
  grad   A Tensor. Must have the same type as orig_input. 4-D. Gradients w.r.t. the output of max_pool.
  ksize   A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.
  strides   A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.
  padding   A string from: "SAME", "VALID", "EXPLICIT". The type of padding algorithm to use.
  explicit_paddings   An optional list of ints. Defaults to [].
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as orig_input.


tf.raw_ops.MaxPoolGradGrad Computes second-order gradients of the maxpooling function.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPoolGradGrad
tf.raw_ops.MaxPoolGradGrad(
    orig_input, orig_output, grad, ksize, strides, padding,
    data_format='NHWC', name=None
)

 Args
  orig_input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The original input tensor.
  orig_output   A Tensor. Must have the same type as orig_input. The original output tensor.
  grad   A Tensor. Must have the same type as orig_input. 4-D. Gradients of gradients w.r.t. the input of max_pool.
  ksize   A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.
  strides   A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as orig_input.


tf.raw_ops.MaxPoolGradGradV2 Computes second-order gradients of the maxpooling function.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPoolGradGradV2
tf.raw_ops.MaxPoolGradGradV2(
    orig_input, orig_output, grad, ksize, strides, padding,
    data_format='NHWC', name=None
)

 Args
  orig_input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The original input tensor.
  orig_output   A Tensor. Must have the same type as orig_input. The original output tensor.
  grad   A Tensor. Must have the same type as orig_input. 4-D. Gradients of gradients w.r.t. the input of max_pool.
  ksize   A Tensor of type int32. The size of the window for each dimension of the input tensor.
  strides   A Tensor of type int32. The stride of the sliding window for each dimension of the input tensor.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as orig_input.


tf.raw_ops.MaxPoolGradGradWithArgmax Computes second-order gradients of the maxpooling function.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPoolGradGradWithArgmax
tf.raw_ops.MaxPoolGradGradWithArgmax(
    input, grad, argmax, ksize, strides, padding, include_batch_in_index=False,
    name=None
)

 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The original input.
  grad   A Tensor. Must have the same type as input. 4-D with shape [batch, height, width, channels]. Gradients w.r.t. the input of max_pool.
  argmax   A Tensor. Must be one of the following types: int32, int64. The indices of the maximum values chosen for each output of max_pool.
  ksize   A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.
  strides   A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  include_batch_in_index   An optional bool. Defaults to False. Whether to include batch dimension in flattened index of argmax.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.MaxPoolGradV2 Computes gradients of the maxpooling function.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPoolGradV2
tf.raw_ops.MaxPoolGradV2(
    orig_input, orig_output, grad, ksize, strides, padding,
    data_format='NHWC', name=None
)

 Args
  orig_input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The original input tensor.
  orig_output   A Tensor. Must have the same type as orig_input. The original output tensor.
  grad   A Tensor. Must have the same type as orig_input. 4-D. Gradients w.r.t. the output of max_pool.
  ksize   A Tensor of type int32. The size of the window for each dimension of the input tensor.
  strides   A Tensor of type int32. The stride of the sliding window for each dimension of the input tensor.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  data_format   An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as orig_input.


tf.raw_ops.MaxPoolGradWithArgmax Computes gradients of the maxpooling function.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPoolGradWithArgmax
tf.raw_ops.MaxPoolGradWithArgmax(
    input, grad, argmax, ksize, strides, padding, include_batch_in_index=False,
    name=None
)

 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The original input.
  grad   A Tensor. Must have the same type as input. 4-D with shape [batch, height, width, channels]. Gradients w.r.t. the output of max_pool.
  argmax   A Tensor. Must be one of the following types: int32, int64. The indices of the maximum values chosen for each output of max_pool.
  ksize   A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.
  strides   A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  include_batch_in_index   An optional bool. Defaults to False. Whether to include batch dimension in flattened index of argmax.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.MaxPoolV2 Performs max pooling on the input.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPoolV2
tf.raw_ops.MaxPoolV2(
    input, ksize, strides, padding, data_format='NHWC', name=None
)

 Args
  input   A Tensor. Must be one of the following types: half, bfloat16, float32, float64, int32, int64, uint8, int16, int8, uint16, qint8. 4-D input to pool over.
  ksize   A Tensor of type int32. The size of the window for each dimension of the input tensor.
  strides   A Tensor of type int32. The stride of the sliding window for each dimension of the input tensor.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  data_format   An optional string from: "NHWC", "NCHW", "NCHW_VECT_C". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.MaxPoolWithArgmax Performs max pooling on the input and outputs both max values and indices.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MaxPoolWithArgmax
tf.raw_ops.MaxPoolWithArgmax(
    input, ksize, strides, padding, Targmax=tf.dtypes.int64,
    include_batch_in_index=False, name=None
)
 The indices in argmax are flattened, so that a maximum value at position [b, y, x, c] becomes flattened index: (y * width + x) * channels + c if include_batch_in_index is False; ((b * height + y) * width + x) * channels + c if include_batch_in_index is True. The indices returned are always in [0, height) x [0, width) before flattening, even if padding is involved and the mathematically correct answer is outside (either negative or too large). This is a bug, but fixing it is difficult to do in a safe backwards compatible way, especially due to flattening.

 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 4-D with shape [batch, height, width, channels]. Input to pool over.
  ksize   A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.
  strides   A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.
  padding   A string from: "SAME", "VALID". The type of padding algorithm to use.
  Targmax   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int64.
  include_batch_in_index   An optional bool. Defaults to False. Whether to include batch dimension in flattened index of argmax.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (output, argmax).     output   A Tensor. Has the same type as input.
  argmax   A Tensor of type Targmax.


tf.raw_ops.Mean Computes the mean of elements across dimensions of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Mean
tf.raw_ops.Mean(
    input, axis, keep_dims=False, name=None
)
 Reduces input along the dimensions given in axis. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is true, the reduced dimensions are retained with length 1.

 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. The tensor to reduce.
  axis   A Tensor. Must be one of the following types: int32, int64. The dimensions to reduce. Must be in the range [-rank(input), rank(input)).
  keep_dims   An optional bool. Defaults to False. If true, retain reduced dimensions with length 1.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.Merge Forwards the value of an available tensor from inputs to output.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Merge
tf.raw_ops.Merge(
    inputs, name=None
)
 Merge waits for at least one of the tensors in inputs to become available. It is usually combined with Switch to implement branching. Merge forwards the first tensor to become available to output, and sets value_index to its index in inputs.

 Args
  inputs   A list of at least 1 Tensor objects with the same type. The input tensors, exactly one of which will become available.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (output, value_index).     output   A Tensor. Has the same type as inputs.
  value_index   A Tensor of type int32.


tf.raw_ops.MergeSummary Merges summaries.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MergeSummary
tf.raw_ops.MergeSummary(
    inputs, name=None
)
 This op creates a Summary protocol buffer that contains the union of all the values in the input summaries. When the Op is run, it reports an InvalidArgument error if multiple values in the summaries to merge use the same tag.

 Args
  inputs   A list of at least 1 Tensor objects with type string. Can be of any shape. Each must contain serialized Summary protocol buffers.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.MergeV2Checkpoints V2 format specific: merges the metadata files of sharded checkpoints. The  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MergeV2Checkpoints
tf.raw_ops.MergeV2Checkpoints(
    checkpoint_prefixes, destination_prefix, delete_old_dirs=True, name=None
)
 result is one logical checkpoint, with one physical metadata file and renamed data files. Intended for "grouping" multiple checkpoints in a sharded checkpoint setup. If delete_old_dirs is true, attempts to delete recursively the dirname of each path in the input checkpoint_prefixes. This is useful when those paths are non user-facing temporary locations.

 Args
  checkpoint_prefixes   A Tensor of type string. prefixes of V2 checkpoints to merge.
  destination_prefix   A Tensor of type string. scalar. The desired final prefix. Allowed to be the same as one of the checkpoint_prefixes.
  delete_old_dirs   An optional bool. Defaults to True. see above.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.Mfcc Transforms a spectrogram into a form that's useful for speech recognition.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Mfcc
tf.raw_ops.Mfcc(
    spectrogram, sample_rate, upper_frequency_limit=4000, lower_frequency_limit=20,
    filterbank_channel_count=40, dct_coefficient_count=13, name=None
)
 Mel Frequency Cepstral Coefficients are a way of representing audio data that's been effective as an input feature for machine learning. They are created by taking the spectrum of a spectrogram (a 'cepstrum'), and discarding some of the higher frequencies that are less significant to the human ear. They have a long history in the speech recognition world, and https://en.wikipedia.org/wiki/Mel-frequency_cepstrum is a good resource to learn more.

 Args
  spectrogram   A Tensor of type float32. Typically produced by the Spectrogram op, with magnitude_squared set to true.
  sample_rate   A Tensor of type int32. How many samples per second the source audio used.
  upper_frequency_limit   An optional float. Defaults to 4000. The highest frequency to use when calculating the ceptstrum.
  lower_frequency_limit   An optional float. Defaults to 20. The lowest frequency to use when calculating the ceptstrum.
  filterbank_channel_count   An optional int. Defaults to 40. Resolution of the Mel bank used internally.
  dct_coefficient_count   An optional int. Defaults to 13. How many output channels to produce per time slice.
  name   A name for the operation (optional).

 Returns   A Tensor of type float32.


tf.raw_ops.Min Computes the minimum of elements across dimensions of a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Min
tf.raw_ops.Min(
    input, axis, keep_dims=False, name=None
)
 Reduces input along the dimensions given in axis. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is true, the reduced dimensions are retained with length 1.

 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64, qint8, quint8, qint32, qint16, quint16. The tensor to reduce.
  axis   A Tensor. Must be one of the following types: int32, int64. The dimensions to reduce. Must be in the range [-rank(input), rank(input)).
  keep_dims   An optional bool. Defaults to False. If true, retain reduced dimensions with length 1.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.Minimum Returns the min of x and y (i.e. x < y ? x : y) element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Minimum
tf.raw_ops.Minimum(
    x, y, name=None
)
 Both inputs are number-type tensors (except complex). minimum expects that both tensors have the same dtype. Examples:
x = tf.constant([0., 0., 0., 0.])
y = tf.constant([-5., -2., 0., 3.])
tf.math.minimum(x, y)
<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-5., -2., 0., 0.], dtype=float32)>
 Note that minimum supports broadcast semantics.
x = tf.constant([-5., 0., 0., 0.])
y = tf.constant([-3.])
tf.math.minimum(x, y)
<tf.Tensor: shape=(4,), dtype=float32, numpy=array([-5., -3., -3., -3.], dtype=float32)>
 If inputs are not tensors, they will be converted to tensors. See tf.convert_to_tensor.
x = tf.constant([-3.], dtype=tf.float32)
tf.math.minimum([-5], x)
<tf.Tensor: shape=(1,), dtype=float32, numpy=array([-5.], dtype=float32)>

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int16, int32, int64.
  y   A Tensor. Must have the same type as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.MirrorPad Pads a tensor with mirrored values.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MirrorPad
tf.raw_ops.MirrorPad(
    input, paddings, mode, name=None
)
 This operation pads a input with mirrored values according to the paddings you specify. paddings is an integer tensor with shape [n, 2], where n is the rank of input. For each dimension D of input, paddings[D, 0] indicates how many values to add before the contents of input in that dimension, and paddings[D, 1] indicates how many values to add after the contents of input in that dimension. Both paddings[D, 0] and paddings[D, 1] must be no greater than input.dim_size(D) (or input.dim_size(D) - 1) if copy_border is true (if false, respectively). The padded size of each dimension D of the output is: paddings(D, 0) + input.dim_size(D) + paddings(D, 1) For example: # 't' is [[1, 2, 3], [4, 5, 6]].
# 'paddings' is [[1, 1]], [2, 2]].
# 'mode' is SYMMETRIC.
# rank of 't' is 2.
pad(t, paddings) ==> [[2, 1, 1, 2, 3, 3, 2]
                      [2, 1, 1, 2, 3, 3, 2]
                      [5, 4, 4, 5, 6, 6, 5]
                      [5, 4, 4, 5, 6, 6, 5]]

 Args
  input   A Tensor. The input tensor to be padded.
  paddings   A Tensor. Must be one of the following types: int32, int64. A two-column matrix specifying the padding sizes. The number of rows must be the same as the rank of input.
  mode   A string from: "REFLECT", "SYMMETRIC". Either REFLECT or SYMMETRIC. In reflect mode the padded regions do not include the borders, while in symmetric mode the padded regions do include the borders. For example, if input is [1, 2, 3] and paddings is [0, 2], then the output is [1, 2, 3, 2, 1] in reflect mode, and it is [1, 2, 3, 3, 2] in symmetric mode.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.MirrorPadGrad Gradient op for MirrorPad op. This op folds a mirror-padded tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MirrorPadGrad
tf.raw_ops.MirrorPadGrad(
    input, paddings, mode, name=None
)
 This operation folds the padded areas of input by MirrorPad according to the paddings you specify. paddings must be the same as paddings argument given to the corresponding MirrorPad op. The folded size of each dimension D of the output is: input.dim_size(D) - paddings(D, 0) - paddings(D, 1) For example: # 't' is [[1, 2, 3], [4, 5, 6], [7, 8, 9]].
# 'paddings' is [[0, 1]], [0, 1]].
# 'mode' is SYMMETRIC.
# rank of 't' is 2.
pad(t, paddings) ==> [[ 1,  5]
                      [11, 28]]

 Args
  input   A Tensor. The input tensor to be folded.
  paddings   A Tensor. Must be one of the following types: int32, int64. A two-column matrix specifying the padding sizes. The number of rows must be the same as the rank of input.
  mode   A string from: "REFLECT", "SYMMETRIC". The mode used in the MirrorPad op.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.Mod Returns element-wise remainder of division. This emulates C semantics in that  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Mod
tf.raw_ops.Mod(
    x, y, name=None
)
 the result here is consistent with a truncating divide. E.g. tf.truncatediv(x, y) * y + truncate_mod(x, y) = x.
Note: Mod supports broadcasting. More about broadcasting here

 Args
  x   A Tensor. Must be one of the following types: int32, int64, half, half, bfloat16, float32, float64.
  y   A Tensor. Must have the same type as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.ModelDataset Identity transformation that models performance.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.ModelDataset
tf.raw_ops.ModelDataset(
    input_dataset, output_types, output_shapes, algorithm=0, cpu_budget=0,
    ram_budget=0, name=None
)
 Identity transformation that models performance.

 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  algorithm   An optional int. Defaults to 0.
  cpu_budget   An optional int. Defaults to 0.
  ram_budget   An optional int. Defaults to 0.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.Mul Returns x * y element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Mul
tf.raw_ops.Mul(
    x, y, name=None
)

Note: Multiply supports broadcasting. More about broadcasting here

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, uint8, int8, uint16, int16, int32, int64, complex64, complex128.
  y   A Tensor. Must have the same type as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.MulNoNan Returns x * y element-wise. Returns zero if y is zero, even if x if infinite or NaN.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MulNoNan
tf.raw_ops.MulNoNan(
    x, y, name=None
)

Note: MulNoNan supports broadcasting. More about broadcasting here

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, complex64, complex128.
  y   A Tensor. Must have the same type as x.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.MultiDeviceIterator Creates a MultiDeviceIterator resource.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MultiDeviceIterator
tf.raw_ops.MultiDeviceIterator(
    devices, shared_name, container, output_types, output_shapes, name=None
)

 Args
  devices   A list of strings that has length >= 1. A list of devices the iterator works across.
  shared_name   A string. If non-empty, this resource will be shared under the given name across multiple sessions.
  container   A string. If non-empty, this resource is placed in the given container. Otherwise, a default container is used.
  output_types   A list of tf.DTypes that has length >= 1. The type list for the return values.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1. The list of shapes being produced.
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.MultiDeviceIteratorFromStringHandle Generates a MultiDeviceIterator resource from its provided string handle.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MultiDeviceIteratorFromStringHandle
tf.raw_ops.MultiDeviceIteratorFromStringHandle(
    string_handle, output_types=[], output_shapes=[], name=None
)

 Args
  string_handle   A Tensor of type string. String representing the resource.
  output_types   An optional list of tf.DTypes. Defaults to []. The type list for the return values.
  output_shapes   An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to []. The list of shapes being produced.
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.MultiDeviceIteratorGetNextFromShard Gets next element for the provided shard number.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MultiDeviceIteratorGetNextFromShard
tf.raw_ops.MultiDeviceIteratorGetNextFromShard(
    multi_device_iterator, shard_num, incarnation_id, output_types, output_shapes,
    name=None
)

 Args
  multi_device_iterator   A Tensor of type resource. A MultiDeviceIterator resource.
  shard_num   A Tensor of type int32. Integer representing which shard to fetch data for.
  incarnation_id   A Tensor of type int64. Which incarnation of the MultiDeviceIterator is running.
  output_types   A list of tf.DTypes that has length >= 1. The type list for the return values.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1. The list of shapes being produced.
  name   A name for the operation (optional).

 Returns   A list of Tensor objects of type output_types.


tf.raw_ops.MultiDeviceIteratorInit Initializes the multi device iterator with the given dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MultiDeviceIteratorInit
tf.raw_ops.MultiDeviceIteratorInit(
    dataset, multi_device_iterator, max_buffer_size, name=None
)

 Args
  dataset   A Tensor of type variant. Dataset to be iterated upon.
  multi_device_iterator   A Tensor of type resource. A MultiDeviceIteratorResource.
  max_buffer_size   A Tensor of type int64. The maximum size of the host side per device buffer to keep.
  name   A name for the operation (optional).

 Returns   A Tensor of type int64.


tf.raw_ops.MultiDeviceIteratorToStringHandle Produces a string handle for the given MultiDeviceIterator.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MultiDeviceIteratorToStringHandle
tf.raw_ops.MultiDeviceIteratorToStringHandle(
    multi_device_iterator, name=None
)

 Args
  multi_device_iterator   A Tensor of type resource. A MultiDeviceIterator resource.
  name   A name for the operation (optional).

 Returns   A Tensor of type string.


tf.raw_ops.Multinomial Draws samples from a multinomial distribution.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Multinomial
tf.raw_ops.Multinomial(
    logits, num_samples, seed=0, seed2=0, output_dtype=tf.dtypes.int64, name=None
)

 Args
  logits   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 2-D Tensor with shape [batch_size, num_classes]. Each slice [i, :] represents the unnormalized log probabilities for all classes.
  num_samples   A Tensor of type int32. 0-D. Number of independent samples to draw for each row slice.
  seed   An optional int. Defaults to 0. If either seed or seed2 is set to be non-zero, the internal random number generator is seeded by the given seed. Otherwise, a random seed is used.
  seed2   An optional int. Defaults to 0. A second seed to avoid seed collision.
  output_dtype   An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int64.
  name   A name for the operation (optional).

 Returns   A Tensor of type output_dtype.


tf.raw_ops.MutableDenseHashTable Creates an empty hash table that uses tensors as the backing store.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MutableDenseHashTable
tf.raw_ops.MutableDenseHashTable(
    empty_key, value_dtype, container='', shared_name='',
    use_node_name_sharing=False, value_shape=[], initial_num_buckets=131072,
    max_load_factor=0.8, name=None
)
 It uses "open addressing" with quadratic reprobing to resolve collisions. This op creates a mutable hash table, specifying the type of its keys and values. Each value must be a scalar. Data can be inserted into the table using the insert operations. It does not support the initialization operation.

 Args
  empty_key   A Tensor. The key used to represent empty key buckets internally. Must not be used in insert or lookup operations.
  value_dtype   A tf.DType. Type of the table values.
  container   An optional string. Defaults to "". If non-empty, this table is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this table is shared under the given name across multiple sessions.
  use_node_name_sharing   An optional bool. Defaults to False.
  value_shape   An optional tf.TensorShape or list of ints. Defaults to []. The shape of each value.
  initial_num_buckets   An optional int. Defaults to 131072. The initial number of hash table buckets. Must be a power to 2.
  max_load_factor   An optional float. Defaults to 0.8. The maximum ratio between number of entries and number of buckets before growing the table. Must be between 0 and 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type mutable string.


tf.raw_ops.MutableDenseHashTableV2 Creates an empty hash table that uses tensors as the backing store.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MutableDenseHashTableV2
tf.raw_ops.MutableDenseHashTableV2(
    empty_key, deleted_key, value_dtype, container='',
    shared_name='', use_node_name_sharing=False, value_shape=[],
    initial_num_buckets=131072, max_load_factor=0.8, name=None
)
 It uses "open addressing" with quadratic reprobing to resolve collisions. This op creates a mutable hash table, specifying the type of its keys and values. Each value must be a scalar. Data can be inserted into the table using the insert operations. It does not support the initialization operation.

 Args
  empty_key   A Tensor. The key used to represent empty key buckets internally. Must not be used in insert or lookup operations.
  deleted_key   A Tensor. Must have the same type as empty_key.
  value_dtype   A tf.DType. Type of the table values.
  container   An optional string. Defaults to "". If non-empty, this table is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this table is shared under the given name across multiple sessions.
  use_node_name_sharing   An optional bool. Defaults to False.
  value_shape   An optional tf.TensorShape or list of ints. Defaults to []. The shape of each value.
  initial_num_buckets   An optional int. Defaults to 131072. The initial number of hash table buckets. Must be a power to 2.
  max_load_factor   An optional float. Defaults to 0.8. The maximum ratio between number of entries and number of buckets before growing the table. Must be between 0 and 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.MutableHashTable Creates an empty hash table.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MutableHashTable
tf.raw_ops.MutableHashTable(
    key_dtype, value_dtype, container='', shared_name='',
    use_node_name_sharing=False, name=None
)
 This op creates a mutable hash table, specifying the type of its keys and values. Each value must be a scalar. Data can be inserted into the table using the insert operations. It does not support the initialization operation.

 Args
  key_dtype   A tf.DType. Type of the table keys.
  value_dtype   A tf.DType. Type of the table values.
  container   An optional string. Defaults to "". If non-empty, this table is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this table is shared under the given name across multiple sessions.
  use_node_name_sharing   An optional bool. Defaults to False. If true and shared_name is empty, the table is shared using the node name.
  name   A name for the operation (optional).

 Returns   A Tensor of type mutable string.


tf.raw_ops.MutableHashTableOfTensors Creates an empty hash table.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MutableHashTableOfTensors
tf.raw_ops.MutableHashTableOfTensors(
    key_dtype, value_dtype, container='', shared_name='',
    use_node_name_sharing=False, value_shape=[], name=None
)
 This op creates a mutable hash table, specifying the type of its keys and values. Each value must be a vector. Data can be inserted into the table using the insert operations. It does not support the initialization operation.

 Args
  key_dtype   A tf.DType. Type of the table keys.
  value_dtype   A tf.DType. Type of the table values.
  container   An optional string. Defaults to "". If non-empty, this table is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this table is shared under the given name across multiple sessions.
  use_node_name_sharing   An optional bool. Defaults to False.
  value_shape   An optional tf.TensorShape or list of ints. Defaults to [].
  name   A name for the operation (optional).

 Returns   A Tensor of type mutable string.


tf.raw_ops.MutableHashTableOfTensorsV2 Creates an empty hash table.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MutableHashTableOfTensorsV2
tf.raw_ops.MutableHashTableOfTensorsV2(
    key_dtype, value_dtype, container='', shared_name='',
    use_node_name_sharing=False, value_shape=[], name=None
)
 This op creates a mutable hash table, specifying the type of its keys and values. Each value must be a vector. Data can be inserted into the table using the insert operations. It does not support the initialization operation.

 Args
  key_dtype   A tf.DType. Type of the table keys.
  value_dtype   A tf.DType. Type of the table values.
  container   An optional string. Defaults to "". If non-empty, this table is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this table is shared under the given name across multiple sessions.
  use_node_name_sharing   An optional bool. Defaults to False.
  value_shape   An optional tf.TensorShape or list of ints. Defaults to [].
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.MutableHashTableV2 Creates an empty hash table.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MutableHashTableV2
tf.raw_ops.MutableHashTableV2(
    key_dtype, value_dtype, container='', shared_name='',
    use_node_name_sharing=False, name=None
)
 This op creates a mutable hash table, specifying the type of its keys and values. Each value must be a scalar. Data can be inserted into the table using the insert operations. It does not support the initialization operation.

 Args
  key_dtype   A tf.DType. Type of the table keys.
  value_dtype   A tf.DType. Type of the table values.
  container   An optional string. Defaults to "". If non-empty, this table is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this table is shared under the given name across multiple sessions.
  use_node_name_sharing   An optional bool. Defaults to False. If true and shared_name is empty, the table is shared using the node name.
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.MutexLock Locks a mutex resource. The output is the lock. So long as the lock tensor  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MutexLock
tf.raw_ops.MutexLock(
    mutex, name=None
)
 is alive, any other request to use MutexLock with this mutex will wait. This is particularly useful for creating a critical section when used in conjunction with MutexLockIdentity:
mutex = mutex_v2(
  shared_name=handle_name, container=container, name=name)

def execute_in_critical_section(fn, *args, **kwargs):
  lock = gen_resource_variable_ops.mutex_lock(mutex)

  with ops.control_dependencies([lock]):
    r = fn(*args, **kwargs)

  with ops.control_dependencies(nest.flatten(r)):
    with ops.colocate_with(mutex):
      ensure_lock_exists = mutex_lock_identity(lock)

    # Make sure that if any element of r is accessed, all of
    # them are executed together.
    r = nest.map_structure(tf.identity, r)

  with ops.control_dependencies([ensure_lock_exists]):
    return nest.map_structure(tf.identity, r)
 While fn is running in the critical section, no other functions which wish to use this critical section may run. Often the use case is that two executions of the same graph, in parallel, wish to run fn; and we wish to ensure that only one of them executes at a time. This is especially important if fn modifies one or more variables at a time. It is also useful if two separate functions must share a resource, but we wish to ensure the usage is exclusive.

 Args
  mutex   A Tensor of type resource. The mutex resource to lock.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.MutexV2 Creates a Mutex resource that can be locked by MutexLock.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.MutexV2
tf.raw_ops.MutexV2(
    container='', shared_name='', name=None
)

 Args
  container   An optional string. Defaults to "". If non-empty, this variable is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this variable is named in the given bucket with this shared_name. Otherwise, the node name is used instead.
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.NcclAllReduce Outputs a tensor containing the reduction across all input tensors.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.NcclAllReduce
tf.raw_ops.NcclAllReduce(
    input, reduction, num_devices, shared_name, name=None
)
 Outputs a tensor containing the reduction across all input tensors passed to ops within the same `shared_name. The graph should be constructed so if one op runs with shared_name value c, then num_devices ops will run with shared_name value c. Failure to do so will cause the graph execution to fail to complete. input: the input to the reduction data: the value of the reduction across all num_devices devices. reduction: the reduction operation to perform. num_devices: The number of devices participating in this reduction. shared_name: Identifier that shared between ops of the same reduction.

 Args
  input   A Tensor. Must be one of the following types: half, float32, float64, int32, int64.
  reduction   A string from: "min", "max", "prod", "sum".
  num_devices   An int.
  shared_name   A string.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.NcclBroadcast Sends input to all devices that are connected to the output.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.NcclBroadcast
tf.raw_ops.NcclBroadcast(
    input, shape, name=None
)
 Sends input to all devices that are connected to the output. The graph should be constructed so that all ops connected to the output have a valid device assignment, and the op itself is assigned one of these devices. input: The input to the broadcast. output: The same as input. shape: The shape of the input tensor.

 Args
  input   A Tensor. Must be one of the following types: half, float32, float64, int32, int64.
  shape   A tf.TensorShape or list of ints.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.NcclReduce Reduces input from num_devices using reduction to a single device.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.NcclReduce
tf.raw_ops.NcclReduce(
    input, reduction, name=None
)
 Reduces input from num_devices using reduction to a single device. The graph should be constructed so that all inputs have a valid device assignment, and the op itself is assigned one of these devices. input: The input to the reduction. data: the value of the reduction across all num_devices devices. reduction: the reduction operation to perform.

 Args
  input   A list of at least 1 Tensor objects with the same type in: half, float32, float64, int32, int64.
  reduction   A string from: "min", "max", "prod", "sum".
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.Ndtri  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Ndtri
tf.raw_ops.Ndtri(
    x, name=None
)

 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.


tf.raw_ops.Neg   Computes numerical negative value element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Neg
tf.raw_ops.Neg(
    x, name=None
)
 I.e., \(y = -x\).

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, int16, int32, int64, complex64, complex128.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.NextAfter Returns the next representable value of x1 in the direction of x2, element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.NextAfter
tf.raw_ops.NextAfter(
    x1, x2, name=None
)
 This operation returns the same result as the C++ std::nextafter function. It can also return a subnormal number.

 Args
  x1   A Tensor. Must be one of the following types: float64, float32.
  x2   A Tensor. Must have the same type as x1.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x1.
 Cpp Compatibility Equivalent to C++ std::nextafter function.


tf.raw_ops.NextIteration Makes its input available to the next iteration.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.NextIteration
tf.raw_ops.NextIteration(
    data, name=None
)

 Args
  data   A Tensor. The tensor to be made available to the next iteration.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as data.


tf.raw_ops.NonDeterministicInts Non-deterministically generates some integers.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.NonDeterministicInts
tf.raw_ops.NonDeterministicInts(
    shape, dtype=tf.dtypes.int64, name=None
)
 This op may use some OS-provided source of non-determinism (e.g. an RNG), so each execution will give different results.

 Args
  shape   A Tensor. The shape of the output tensor.
  dtype   An optional tf.DType. Defaults to tf.int64. The type of the output.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.NonMaxSuppression Greedily selects a subset of bounding boxes in descending order of score,  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.NonMaxSuppression
tf.raw_ops.NonMaxSuppression(
    boxes, scores, max_output_size, iou_threshold=0.5, name=None
)
 pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm is agnostic to where the origin is in the coordinate system. Note that this algorithm is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the tf.gather operation. For example: selected_indices = tf.image.non_max_suppression( boxes, scores, max_output_size, iou_threshold) selected_boxes = tf.gather(boxes, selected_indices)

 Args
  boxes   A Tensor of type float32. A 2-D float tensor of shape [num_boxes, 4].
  scores   A Tensor of type float32. A 1-D float tensor of shape [num_boxes] representing a single score corresponding to each box (each row of boxes).
  max_output_size   A Tensor of type int32. A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression.
  iou_threshold   An optional float. Defaults to 0.5. A float representing the threshold for deciding whether boxes overlap too much with respect to IOU.
  name   A name for the operation (optional).

 Returns   A Tensor of type int32.


tf.raw_ops.NonMaxSuppressionV2 Greedily selects a subset of bounding boxes in descending order of score,  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.NonMaxSuppressionV2
tf.raw_ops.NonMaxSuppressionV2(
    boxes, scores, max_output_size, iou_threshold, name=None
)
 pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm is agnostic to where the origin is in the coordinate system. Note that this algorithm is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the tf.gather operation. For example: selected_indices = tf.image.non_max_suppression_v2( boxes, scores, max_output_size, iou_threshold) selected_boxes = tf.gather(boxes, selected_indices)

 Args
  boxes   A Tensor. Must be one of the following types: half, float32. A 2-D float tensor of shape [num_boxes, 4].
  scores   A Tensor. Must have the same type as boxes. A 1-D float tensor of shape [num_boxes] representing a single score corresponding to each box (each row of boxes).
  max_output_size   A Tensor of type int32. A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression.
  iou_threshold   A Tensor. Must be one of the following types: half, float32. A 0-D float tensor representing the threshold for deciding whether boxes overlap too much with respect to IOU.
  name   A name for the operation (optional).

 Returns   A Tensor of type int32.


tf.raw_ops.NonMaxSuppressionV3 Greedily selects a subset of bounding boxes in descending order of score,  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.NonMaxSuppressionV3
tf.raw_ops.NonMaxSuppressionV3(
    boxes, scores, max_output_size, iou_threshold, score_threshold, name=None
)
 pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than score_threshold are removed. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the tf.gather operation. For example: selected_indices = tf.image.non_max_suppression_v2( boxes, scores, max_output_size, iou_threshold, score_threshold) selected_boxes = tf.gather(boxes, selected_indices)

 Args
  boxes   A Tensor. Must be one of the following types: half, float32. A 2-D float tensor of shape [num_boxes, 4].
  scores   A Tensor. Must have the same type as boxes. A 1-D float tensor of shape [num_boxes] representing a single score corresponding to each box (each row of boxes).
  max_output_size   A Tensor of type int32. A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression.
  iou_threshold   A Tensor. Must be one of the following types: half, float32. A 0-D float tensor representing the threshold for deciding whether boxes overlap too much with respect to IOU.
  score_threshold   A Tensor. Must have the same type as iou_threshold. A 0-D float tensor representing the threshold for deciding when to remove boxes based on score.
  name   A name for the operation (optional).

 Returns   A Tensor of type int32.


tf.raw_ops.NonMaxSuppressionV4 Greedily selects a subset of bounding boxes in descending order of score,  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.NonMaxSuppressionV4
tf.raw_ops.NonMaxSuppressionV4(
    boxes, scores, max_output_size, iou_threshold, score_threshold,
    pad_to_max_output_size=False, name=None
)
 pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than score_threshold are removed. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the tf.gather operation. For example: selected_indices = tf.image.non_max_suppression_v2( boxes, scores, max_output_size, iou_threshold, score_threshold) selected_boxes = tf.gather(boxes, selected_indices)

 Args
  boxes   A Tensor. Must be one of the following types: half, float32. A 2-D float tensor of shape [num_boxes, 4].
  scores   A Tensor. Must have the same type as boxes. A 1-D float tensor of shape [num_boxes] representing a single score corresponding to each box (each row of boxes).
  max_output_size   A Tensor of type int32. A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression.
  iou_threshold   A Tensor. Must be one of the following types: half, float32. A 0-D float tensor representing the threshold for deciding whether boxes overlap too much with respect to IOU.
  score_threshold   A Tensor. Must have the same type as iou_threshold. A 0-D float tensor representing the threshold for deciding when to remove boxes based on score.
  pad_to_max_output_size   An optional bool. Defaults to False. If true, the output selected_indices is padded to be of length max_output_size. Defaults to false.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (selected_indices, valid_outputs).     selected_indices   A Tensor of type int32.
  valid_outputs   A Tensor of type int32.


tf.raw_ops.NonMaxSuppressionV5 Greedily selects a subset of bounding boxes in descending order of score,  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.NonMaxSuppressionV5
tf.raw_ops.NonMaxSuppressionV5(
    boxes, scores, max_output_size, iou_threshold, score_threshold, soft_nms_sigma,
    pad_to_max_output_size=False, name=None
)
 pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes with score less than score_threshold are removed. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm is agnostic to where the origin is in the coordinate system and more generally is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the tf.gather operation. For example: selected_indices = tf.image.non_max_suppression_v2( boxes, scores, max_output_size, iou_threshold, score_threshold) selected_boxes = tf.gather(boxes, selected_indices) This op also supports a Soft-NMS (with Gaussian weighting) mode (c.f. Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score of other overlapping boxes instead of directly causing them to be pruned. To enable this Soft-NMS mode, set the soft_nms_sigma parameter to be larger than 0.

 Args
  boxes   A Tensor. Must be one of the following types: half, float32. A 2-D float tensor of shape [num_boxes, 4].
  scores   A Tensor. Must have the same type as boxes. A 1-D float tensor of shape [num_boxes] representing a single score corresponding to each box (each row of boxes).
  max_output_size   A Tensor of type int32. A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression.
  iou_threshold   A Tensor. Must have the same type as boxes. A 0-D float tensor representing the threshold for deciding whether boxes overlap too much with respect to IOU.
  score_threshold   A Tensor. Must have the same type as boxes. A 0-D float tensor representing the threshold for deciding when to remove boxes based on score.
  soft_nms_sigma   A Tensor. Must have the same type as boxes. A 0-D float tensor representing the sigma parameter for Soft NMS; see Bodla et al (c.f. https://arxiv.org/abs/1704.04503). When soft_nms_sigma=0.0 (which is default), we fall back to standard (hard) NMS.
  pad_to_max_output_size   An optional bool. Defaults to False. If true, the output selected_indices is padded to be of length max_output_size. Defaults to false.
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (selected_indices, selected_scores, valid_outputs).     selected_indices   A Tensor of type int32.
  selected_scores   A Tensor. Has the same type as boxes.
  valid_outputs   A Tensor of type int32.


tf.raw_ops.NonMaxSuppressionWithOverlaps Greedily selects a subset of bounding boxes in descending order of score,  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.NonMaxSuppressionWithOverlaps
tf.raw_ops.NonMaxSuppressionWithOverlaps(
    overlaps, scores, max_output_size, overlap_threshold, score_threshold, name=None
)
 pruning away boxes that have high overlaps with previously selected boxes. Bounding boxes with score less than score_threshold are removed. N-by-n overlap values are supplied as square matrix, which allows for defining a custom overlap criterium (eg. intersection over union, intersection over area, etc.). The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the tf.gather operation. For example: selected_indices = tf.image.non_max_suppression_with_overlaps( overlaps, scores, max_output_size, overlap_threshold, score_threshold) selected_boxes = tf.gather(boxes, selected_indices)

 Args
  overlaps   A Tensor of type float32. A 2-D float tensor of shape [num_boxes, num_boxes] representing the n-by-n box overlap values.
  scores   A Tensor of type float32. A 1-D float tensor of shape [num_boxes] representing a single score corresponding to each box (each row of boxes).
  max_output_size   A Tensor of type int32. A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression.
  overlap_threshold   A Tensor of type float32. A 0-D float tensor representing the threshold for deciding whether boxes overlap too.
  score_threshold   A Tensor of type float32. A 0-D float tensor representing the threshold for deciding when to remove boxes based on score.
  name   A name for the operation (optional).

 Returns   A Tensor of type int32.


tf.raw_ops.NonSerializableDataset  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.NonSerializableDataset
tf.raw_ops.NonSerializableDataset(
    input_dataset, output_types, output_shapes, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.NoOp Does nothing. Only useful as a placeholder for control edges.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.NoOp
tf.raw_ops.NoOp(
    name=None
)

 Args
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.NotEqual Returns the truth value of (x != y) element-wise.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.NotEqual
tf.raw_ops.NotEqual(
    x, y, incompatible_shape_error=True, name=None
)

Note: NotEqual supports broadcasting. More about broadcasting here

 Args
  x   A Tensor.
  y   A Tensor. Must have the same type as x.
  incompatible_shape_error   An optional bool. Defaults to True.
  name   A name for the operation (optional).

 Returns   A Tensor of type bool.


tf.raw_ops.NthElement Finds values of the n-th order statistic for the last dimension.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.NthElement
tf.raw_ops.NthElement(
    input, n, reverse=False, name=None
)
 If the input is a vector (rank-1), finds the entries which is the nth-smallest value in the vector and outputs their values as scalar tensor. For matrices (resp. higher rank input), computes the entries which is the nth-smallest value in each row (resp. vector along the last dimension). Thus, values.shape = input.shape[:-1]

 Args
  input   A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. 1-D or higher with last dimension at least n+1.
  n   A Tensor of type int32. 0-D. Position of sorted vector to select along the last dimension (along each row for matrices). Valid range of n is [0, input.shape[:-1])
  reverse   An optional bool. Defaults to False. When set to True, find the nth-largest value in the vector and vice versa.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.OneHot Returns a one-hot tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OneHot
tf.raw_ops.OneHot(
    indices, depth, on_value, off_value, axis=-1, name=None
)
 The locations represented by indices in indices take value on_value, while all other locations take value off_value. If the input indices is rank N, the output will have rank N+1, The new axis is created at dimension axis (default: the new axis is appended at the end). If indices is a scalar the output shape will be a vector of length depth. If indices is a vector of length features, the output shape will be: features x depth if axis == -1
depth x features if axis == 0
 If indices is a matrix (batch) with shape [batch, features], the output shape will be: batch x features x depth if axis == -1
batch x depth x features if axis == 1
depth x batch x features if axis == 0
 Examples Suppose that indices = [0, 2, -1, 1]
depth = 3
on_value = 5.0
off_value = 0.0
axis = -1
 Then output is [4 x 3]: output =
  [5.0 0.0 0.0]  // one_hot(0)
  [0.0 0.0 5.0]  // one_hot(2)
  [0.0 0.0 0.0]  // one_hot(-1)
  [0.0 5.0 0.0]  // one_hot(1)
 Suppose that indices = [0, 2, -1, 1]
depth = 3
on_value = 0.0
off_value = 3.0
axis = 0
 Then output is [3 x 4]: output =
  [0.0 3.0 3.0 3.0]
  [3.0 3.0 3.0 0.0]
  [3.0 3.0 3.0 3.0]
  [3.0 0.0 3.0 3.0]
//  ^                one_hot(0)
//      ^            one_hot(2)
//          ^        one_hot(-1)
//              ^    one_hot(1)
 Suppose that indices = [[0, 2], [1, -1]]
depth = 3
on_value = 1.0
off_value = 0.0
axis = -1
 Then output is [2 x 2 x 3]: output =
  [
    [1.0, 0.0, 0.0]  // one_hot(0)
    [0.0, 0.0, 1.0]  // one_hot(2)
  ][
    [0.0, 1.0, 0.0]  // one_hot(1)
    [0.0, 0.0, 0.0]  // one_hot(-1)
  ]

 Args
  indices   A Tensor. Must be one of the following types: uint8, int32, int64. A tensor of indices.
  depth   A Tensor of type int32. A scalar defining the depth of the one hot dimension.
  on_value   A Tensor. A scalar defining the value to fill in output when indices[j] = i.
  off_value   A Tensor. Must have the same type as on_value. A scalar defining the value to fill in output when indices[j] != i.
  axis   An optional int. Defaults to -1. The axis to fill (default: -1, a new inner-most axis).
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as on_value.


tf.raw_ops.OneShotIterator Makes a "one-shot" iterator that can be iterated only once.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OneShotIterator
tf.raw_ops.OneShotIterator(
    dataset_factory, output_types, output_shapes, container='',
    shared_name='', name=None
)
 A one-shot iterator bundles the logic for defining the dataset and the state of the iterator in a single op, which allows simple input pipelines to be defined without an additional initialization ("MakeIterator") step. One-shot iterators have the following limitations:  They do not support parameterization: all logic for creating the underlying dataset must be bundled in the dataset_factory function. They are not resettable. Once a one-shot iterator reaches the end of its underlying dataset, subsequent "IteratorGetNext" operations on that iterator will always produce an OutOfRange error.  For greater flexibility, use "Iterator" and "MakeIterator" to define an iterator using an arbitrary subgraph, which may capture tensors (including fed values) as parameters, and which may be reset multiple times by rerunning "MakeIterator".

 Args
  dataset_factory   A function decorated with @Defun. A function of type () -> DT_VARIANT, where the returned DT_VARIANT is a dataset.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  container   An optional string. Defaults to "".
  shared_name   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.OnesLike Returns a tensor of ones with the same shape and type as x.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OnesLike
tf.raw_ops.OnesLike(
    x, name=None
)

 Args
  x   A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int8, uint8, int16, uint16, int32, int64, complex64, complex128, bool. a tensor of type T.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as x.


tf.raw_ops.OptimizeDataset Creates a dataset by applying optimizations to input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OptimizeDataset
tf.raw_ops.OptimizeDataset(
    input_dataset, optimizations, output_types, output_shapes,
    optimization_configs=[], name=None
)
 Creates a dataset by applying optimizations to input_dataset.

 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.
  optimizations   A Tensor of type string. A tf.string vector tf.Tensor identifying optimizations to use.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  optimization_configs   An optional list of strings. Defaults to [].
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.OptimizeDatasetV2 Creates a dataset by applying related optimizations to input_dataset.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OptimizeDatasetV2
tf.raw_ops.OptimizeDatasetV2(
    input_dataset, optimizations_enabled, optimizations_disabled,
    optimizations_default, output_types, output_shapes, optimization_configs=[],
    name=None
)
 Creates a dataset by applying related optimizations to input_dataset.

 Args
  input_dataset   A Tensor of type variant. A variant tensor representing the input dataset.
  optimizations_enabled   A Tensor of type string. A tf.string vector tf.Tensor identifying user enabled optimizations.
  optimizations_disabled   A Tensor of type string. A tf.string vector tf.Tensor identifying user disabled optimizations.
  optimizations_default   A Tensor of type string. A tf.string vector tf.Tensor identifying optimizations by default.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  optimization_configs   An optional list of strings. Defaults to [].
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.OptionalFromValue Constructs an Optional variant from a tuple of tensors.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OptionalFromValue
tf.raw_ops.OptionalFromValue(
    components, name=None
)

 Args
  components   A list of Tensor objects.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.OptionalGetValue Returns the value stored in an Optional variant or raises an error if none exists.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OptionalGetValue
tf.raw_ops.OptionalGetValue(
    optional, output_types, output_shapes, name=None
)

 Args
  optional   A Tensor of type variant.
  output_types   A list of tf.DTypes that has length >= 1.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A list of Tensor objects of type output_types.


tf.raw_ops.OptionalHasValue Returns true if and only if the given Optional variant has a value.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OptionalHasValue
tf.raw_ops.OptionalHasValue(
    optional, name=None
)

 Args
  optional   A Tensor of type variant.
  name   A name for the operation (optional).

 Returns   A Tensor of type bool.


tf.raw_ops.OptionalNone Creates an Optional variant with no value.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OptionalNone
tf.raw_ops.OptionalNone(
    name=None
)

 Args
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.OrderedMapClear Op removes all elements in the underlying container.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OrderedMapClear
tf.raw_ops.OrderedMapClear(
    dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)

 Args
  dtypes   A list of tf.DTypes.
  capacity   An optional int that is >= 0. Defaults to 0.
  memory_limit   An optional int that is >= 0. Defaults to 0.
  container   An optional string. Defaults to "".
  shared_name   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.OrderedMapIncompleteSize Op returns the number of incomplete elements in the underlying container.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OrderedMapIncompleteSize
tf.raw_ops.OrderedMapIncompleteSize(
    dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)

 Args
  dtypes   A list of tf.DTypes.
  capacity   An optional int that is >= 0. Defaults to 0.
  memory_limit   An optional int that is >= 0. Defaults to 0.
  container   An optional string. Defaults to "".
  shared_name   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor of type int32.


tf.raw_ops.OrderedMapPeek Op peeks at the values at the specified key. If the  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OrderedMapPeek
tf.raw_ops.OrderedMapPeek(
    key, indices, dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)
 underlying container does not contain this key this op will block until it does. This Op is optimized for performance.

 Args
  key   A Tensor of type int64.
  indices   A Tensor of type int32.
  dtypes   A list of tf.DTypes that has length >= 1.
  capacity   An optional int that is >= 0. Defaults to 0.
  memory_limit   An optional int that is >= 0. Defaults to 0.
  container   An optional string. Defaults to "".
  shared_name   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A list of Tensor objects of type dtypes.


tf.raw_ops.OrderedMapSize Op returns the number of elements in the underlying container.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OrderedMapSize
tf.raw_ops.OrderedMapSize(
    dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)

 Args
  dtypes   A list of tf.DTypes.
  capacity   An optional int that is >= 0. Defaults to 0.
  memory_limit   An optional int that is >= 0. Defaults to 0.
  container   An optional string. Defaults to "".
  shared_name   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A Tensor of type int32.


tf.raw_ops.OrderedMapStage Stage (key, values) in the underlying container which behaves like a ordered  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OrderedMapStage
tf.raw_ops.OrderedMapStage(
    key, indices, values, dtypes, capacity=0, memory_limit=0,
    container='', shared_name='', name=None
)
 associative container. Elements are ordered by key.

 Args
  key   A Tensor of type int64. int64
  indices   A Tensor of type int32.
  values   A list of Tensor objects. a list of tensors dtypes A list of data types that inserted values should adhere to.
  dtypes   A list of tf.DTypes.
  capacity   An optional int that is >= 0. Defaults to 0. Maximum number of elements in the Staging Area. If > 0, inserts on the container will block when the capacity is reached.
  memory_limit   An optional int that is >= 0. Defaults to 0.
  container   An optional string. Defaults to "". If non-empty, this queue is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". It is necessary to match this name to the matching Unstage Op.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.OrderedMapUnstage Op removes and returns the values associated with the key  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OrderedMapUnstage
tf.raw_ops.OrderedMapUnstage(
    key, indices, dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)
 from the underlying container. If the underlying container does not contain this key, the op will block until it does.

 Args
  key   A Tensor of type int64.
  indices   A Tensor of type int32.
  dtypes   A list of tf.DTypes that has length >= 1.
  capacity   An optional int that is >= 0. Defaults to 0.
  memory_limit   An optional int that is >= 0. Defaults to 0.
  container   An optional string. Defaults to "".
  shared_name   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A list of Tensor objects of type dtypes.


tf.raw_ops.OrderedMapUnstageNoKey Op removes and returns the (key, value) element with the smallest  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OrderedMapUnstageNoKey
tf.raw_ops.OrderedMapUnstageNoKey(
    indices, dtypes, capacity=0, memory_limit=0, container='',
    shared_name='', name=None
)
 key from the underlying container. If the underlying container does not contain elements, the op will block until it does.

 Args
  indices   A Tensor of type int32.
  dtypes   A list of tf.DTypes that has length >= 1.
  capacity   An optional int that is >= 0. Defaults to 0.
  memory_limit   An optional int that is >= 0. Defaults to 0.
  container   An optional string. Defaults to "".
  shared_name   An optional string. Defaults to "".
  name   A name for the operation (optional).

 Returns   A tuple of Tensor objects (key, values).     key   A Tensor of type int64.
  values   A list of Tensor objects of type dtypes.


tf.raw_ops.OutfeedDequeue Retrieves a single tensor from the computation outfeed.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OutfeedDequeue
tf.raw_ops.OutfeedDequeue(
    dtype, shape, device_ordinal=-1, name=None
)
 This operation will block indefinitely until data is available.

 Args
  dtype   A tf.DType. The type of elements in the tensor.
  shape   A tf.TensorShape or list of ints. The shape of the tensor.
  device_ordinal   An optional int. Defaults to -1. The TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device.
  name   A name for the operation (optional).

 Returns   A Tensor of type dtype.


tf.raw_ops.OutfeedDequeueTuple Retrieve multiple values from the computation outfeed.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OutfeedDequeueTuple
tf.raw_ops.OutfeedDequeueTuple(
    dtypes, shapes, device_ordinal=-1, name=None
)
 This operation will block indefinitely until data is available. Output i corresponds to XLA tuple element i.

 Args
  dtypes   A list of tf.DTypes that has length >= 1. The element types of each element in outputs.
  shapes   A list of shapes (each a tf.TensorShape or list of ints). The shapes of each tensor in outputs.
  device_ordinal   An optional int. Defaults to -1. The TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device.
  name   A name for the operation (optional).

 Returns   A list of Tensor objects of type dtypes.


tf.raw_ops.OutfeedDequeueTupleV2 Retrieve multiple values from the computation outfeed. Device ordinal is a  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OutfeedDequeueTupleV2
tf.raw_ops.OutfeedDequeueTupleV2(
    device_ordinal, dtypes, shapes, name=None
)
 tensor allowing dynamic outfeed. This operation will block indefinitely until data is available. Output i corresponds to XLA tuple element i. Args: device_ordinal: A Tensor of type int32. An int scalar tensor, representing the TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device. dtypes: A list of tf.DTypes that has length >= 1. The element types of each element in outputs. shapes: A list of shapes (each a tf.TensorShape or list of ints). The shapes of each tensor in outputs. name: A name for the operation (optional). Returns: A list of Tensor objects of type dtypes.


tf.raw_ops.OutfeedDequeueV2 Retrieves a single tensor from the computation outfeed. Device ordinal is a  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OutfeedDequeueV2
tf.raw_ops.OutfeedDequeueV2(
    device_ordinal, dtype, shape, name=None
)
 tensor allowing dynamic outfeed. This operation will block indefinitely until data is available. Args: device_ordinal: A Tensor of type int32. An int scalar tensor, representing the TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device. dtype: A tf.DType. The type of elements in the tensor. shape: A tf.TensorShape or list of ints. The shape of the tensor. name: A name for the operation (optional). Returns: A Tensor of type dtype.


tf.raw_ops.OutfeedEnqueue Enqueue a Tensor on the computation outfeed.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OutfeedEnqueue
tf.raw_ops.OutfeedEnqueue(
    input, name=None
)

 Args
  input   A Tensor. A tensor that will be inserted into the outfeed queue.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.OutfeedEnqueueTuple Enqueue multiple Tensor values on the computation outfeed.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.OutfeedEnqueueTuple
tf.raw_ops.OutfeedEnqueueTuple(
    inputs, name=None
)

 Args
  inputs   A list of Tensor objects. A list of tensors that will be inserted into the outfeed queue as an XLA tuple.
  name   A name for the operation (optional).

 Returns   The created Operation.


tf.raw_ops.Pack Packs a list of N rank-R tensors into one rank-(R+1) tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Pack
tf.raw_ops.Pack(
    values, axis=0, name=None
)
 Packs the N tensors in values into a tensor with rank one higher than each tensor in values, by packing them along the axis dimension. Given a list of tensors of shape (A, B, C); if axis == 0 then the output tensor will have the shape (N, A, B, C). if axis == 1 then the output tensor will have the shape (A, N, B, C). Etc. For example: # 'x' is [1, 4]
# 'y' is [2, 5]
# 'z' is [3, 6]
pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.
pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
 This is the opposite of unpack.

 Args
  values   A list of at least 1 Tensor objects with the same type. Must be of same shape and type.
  axis   An optional int. Defaults to 0. Dimension along which to pack. Negative values wrap around, so the valid range is [-(R+1), R+1).
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as values.


tf.raw_ops.Pad Pads a tensor with zeros.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.Pad
tf.raw_ops.Pad(
    input, paddings, name=None
)
 This operation pads a input with zeros according to the paddings you specify. paddings is an integer tensor with shape [Dn, 2], where n is the rank of input. For each dimension D of input, paddings[D, 0] indicates how many zeros to add before the contents of input in that dimension, and paddings[D, 1] indicates how many zeros to add after the contents of input in that dimension. The padded size of each dimension D of the output is: paddings(D, 0) + input.dim_size(D) + paddings(D, 1) For example: # 't' is [[1, 1], [2, 2]]
# 'paddings' is [[1, 1], [2, 2]]
# rank of 't' is 2
pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
                      [0, 0, 1, 1, 0, 0]
                      [0, 0, 2, 2, 0, 0]
                      [0, 0, 0, 0, 0, 0]]

 Args
  input   A Tensor.
  paddings   A Tensor. Must be one of the following types: int32, int64.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.


tf.raw_ops.PaddedBatchDataset Creates a dataset that batches and pads batch_size elements from the input.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.PaddedBatchDataset
tf.raw_ops.PaddedBatchDataset(
    input_dataset, batch_size, padded_shapes, padding_values, output_shapes,
    name=None
)

 Args
  input_dataset   A Tensor of type variant.
  batch_size   A Tensor of type int64. A scalar representing the number of elements to accumulate in a batch.
  padded_shapes   A list of at least 1 Tensor objects with type int64. A list of int64 tensors representing the desired padded shapes of the corresponding output components. These shapes may be partially specified, using -1 to indicate that a particular dimension should be padded to the maximum size of all batch elements.
  padding_values   A list of Tensor objects. A list of scalars containing the padding value to use for each of the outputs.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.PaddedBatchDatasetV2 Creates a dataset that batches and pads batch_size elements from the input.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.PaddedBatchDatasetV2
tf.raw_ops.PaddedBatchDatasetV2(
    input_dataset, batch_size, padded_shapes, padding_values, drop_remainder,
    output_shapes, parallel_copy=False, name=None
)

 Args
  input_dataset   A Tensor of type variant.
  batch_size   A Tensor of type int64. A scalar representing the number of elements to accumulate in a batch.
  padded_shapes   A list of at least 1 Tensor objects with type int64. A list of int64 tensors representing the desired padded shapes of the corresponding output components. These shapes may be partially specified, using -1 to indicate that a particular dimension should be padded to the maximum size of all batch elements.
  padding_values   A list of Tensor objects. A list of scalars containing the padding value to use for each of the outputs.
  drop_remainder   A Tensor of type bool. A scalar representing whether the last batch should be dropped in case its size is smaller than desired.
  output_shapes   A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
  parallel_copy   An optional bool. Defaults to False.
  name   A name for the operation (optional).

 Returns   A Tensor of type variant.


tf.raw_ops.PaddingFIFOQueue A queue that produces elements in first-in first-out order.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.PaddingFIFOQueue
tf.raw_ops.PaddingFIFOQueue(
    component_types, shapes=[], capacity=-1, container='',
    shared_name='', name=None
)
 Variable-size shapes are allowed by setting the corresponding shape dimensions to 0 in the shape attr. In this case DequeueMany will pad up to the maximum size of any given element in the minibatch. See below for details.

 Args
  component_types   A list of tf.DTypes that has length >= 1. The type of each component in a value.
  shapes   An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to []. The shape of each component in a value. The length of this attr must be either 0 or the same as the length of component_types. Shapes of fixed rank but variable size are allowed by setting any shape dimension to -1. In this case, the inputs' shape may vary along the given dimension, and DequeueMany will pad the given dimension with zeros up to the maximum shape of all elements in the given batch. If the length of this attr is 0, different queue elements may have different ranks and shapes, but only one element may be dequeued at a time.
  capacity   An optional int. Defaults to -1. The upper bound on the number of elements in this queue. Negative numbers mean no limit.
  container   An optional string. Defaults to "". If non-empty, this queue is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this queue will be shared under the given name across multiple sessions.
  name   A name for the operation (optional).

 Returns   A Tensor of type mutable string.


tf.raw_ops.PaddingFIFOQueueV2 A queue that produces elements in first-in first-out order.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.PaddingFIFOQueueV2
tf.raw_ops.PaddingFIFOQueueV2(
    component_types, shapes=[], capacity=-1, container='',
    shared_name='', name=None
)
 Variable-size shapes are allowed by setting the corresponding shape dimensions to 0 in the shape attr. In this case DequeueMany will pad up to the maximum size of any given element in the minibatch. See below for details.

 Args
  component_types   A list of tf.DTypes that has length >= 1. The type of each component in a value.
  shapes   An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to []. The shape of each component in a value. The length of this attr must be either 0 or the same as the length of component_types. Shapes of fixed rank but variable size are allowed by setting any shape dimension to -1. In this case, the inputs' shape may vary along the given dimension, and DequeueMany will pad the given dimension with zeros up to the maximum shape of all elements in the given batch. If the length of this attr is 0, different queue elements may have different ranks and shapes, but only one element may be dequeued at a time.
  capacity   An optional int. Defaults to -1. The upper bound on the number of elements in this queue. Negative numbers mean no limit.
  container   An optional string. Defaults to "". If non-empty, this queue is placed in the given container. Otherwise, a default container is used.
  shared_name   An optional string. Defaults to "". If non-empty, this queue will be shared under the given name across multiple sessions.
  name   A name for the operation (optional).

 Returns   A Tensor of type resource.


tf.raw_ops.PadV2 Pads a tensor.  View aliases  Compat aliases for migration
See Migration guide for more details. tf.compat.v1.raw_ops.PadV2
tf.raw_ops.PadV2(
    input, paddings, constant_values, name=None
)
 This operation pads input according to the paddings and constant_values you specify. paddings is an integer tensor with shape [Dn, 2], where n is the rank of input. For each dimension D of input, paddings[D, 0] indicates how many padding values to add before the contents of input in that dimension, and paddings[D, 1] indicates how many padding values to add after the contents of input in that dimension. constant_values is a scalar tensor of the same type as input that indicates the value to use for padding input. The padded size of each dimension D of the output is: paddings(D, 0) + input.dim_size(D) + paddings(D, 1) For example: # 't' is [[1, 1], [2, 2]]
# 'paddings' is [[1, 1], [2, 2]]
# 'constant_values' is 0
# rank of 't' is 2
pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
                      [0, 0, 1, 1, 0, 0]
                      [0, 0, 2, 2, 0, 0]
                      [0, 0, 0, 0, 0, 0]]

 Args
  input   A Tensor.
  paddings   A Tensor. Must be one of the following types: int32, int64.
  constant_values   A Tensor. Must have the same type as input.
  name   A name for the operation (optional).

 Returns   A Tensor. Has the same type as input.